https://cyborganthropology.com/index.php?action=history&feed=atom&title=Machine_LearningMachine Learning - Revision history2026-04-22T09:20:36ZRevision history for this page on the wikiMediaWiki 1.45.3https://cyborganthropology.com/index.php?title=Machine_Learning&diff=5739&oldid=prevCaseorganic at 19:52, 16 December 20112011-12-16T19:52:09Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Image:machine-learning-maggie-nichols.jpg|center|600px]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Image:machine-learning-maggie-nichols.jpg|center|600px]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images.<ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf Accessed Oct 2011.</ref></div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images.<ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf Accessed Oct 2011.</ref></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not. <ins style="font-weight: bold; text-decoration: none;">Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which was able to improve its results over time based on which results the visitor clicked on.<ref>Ibid.</ref> </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which was able to improve its results over time based on which results the visitor clicked on.<ref>Ibid.</ref> </div></td></tr>
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</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=5738&oldid=prevCaseorganic at 19:49, 16 December 20112011-12-16T19:49:03Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images.<ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf</ref></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images.<ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf <ins style="font-weight: bold; text-decoration: none;">Accessed Oct 2011.</ins></ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled ''An Inductive Inference Machine''.<ref>Solomonoffm, R. J. An Inductive Reference Machine. Technical Research Group, New York City. http://world.std.com/~rjs/indinf56.pdf</ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled ''An Inductive Inference Machine''.<ref>Solomonoffm, R. J. An Inductive Reference Machine. Technical Research Group, New York City. http://world.std.com/~rjs/indinf56.pdf <ins style="font-weight: bold; text-decoration: none;">Accessed Oct 2011.</ins></ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td></tr>
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</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=5216&oldid=prevCaseorganic at 18:47, 25 September 20112011-09-25T18:47:58Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=5214&oldid=prevCaseorganic at 18:47, 25 September 20112011-09-25T18:47:24Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Definition===</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td></tr>
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</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4660&oldid=prevCaseorganic at 21:22, 2 July 20112011-07-02T21:22:59Z<p></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:22, 2 July 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which was able to improve its results over time based on which results the visitor <del style="font-weight: bold; text-decoration: none;">clicks </del>on.<ref>Ibid.</ref> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which was able to improve its results over time based on which results the visitor <ins style="font-weight: bold; text-decoration: none;">clicked </ins>on.<ref>Ibid.</ref> </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4659&oldid=prevCaseorganic at 21:22, 2 July 20112011-07-02T21:22:52Z<p></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:22, 2 July 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which <del style="font-weight: bold; text-decoration: none;">improves </del>its results over time based on which results the visitor clicks on.<ref>Ibid.</ref> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which <ins style="font-weight: bold; text-decoration: none;">was able to improve </ins>its results over time based on which results the visitor clicks on.<ref>Ibid.</ref> </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4658&oldid=prevCaseorganic at 21:22, 2 July 20112011-07-02T21:22:34Z<p></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:22, 2 July 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program can be trained from security camera images to recognize whether a person is present in the frame. Someone would first train the program by giving it several images without a person present in the frame as the negative input, and then give it several images with a person in the frame as the positive input. The program would then be able to classify future images it is given with reasonable accuracy. This would allow a computer to take an action based on whether it sees a person or not.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype,[http://www.cs.cornell.edu/~tj/striver/ STRIVER], which improves its results over time based on which results the visitor clicks on.<ref>Ibid.</ref> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>More recently, machine learning has been applied to create search engine algorithms. Researchers at Cornell University created a prototype, [http://www.cs.cornell.edu/~tj/striver/ STRIVER], which improves its results over time based on which results the visitor clicks on.<ref>Ibid.</ref> </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4657&oldid=prevCaseorganic at 21:22, 2 July 20112011-07-02T21:22:22Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled <del style="font-weight: bold; text-decoration: none;">"</del>An Inductive Inference Machine.<del style="font-weight: bold; text-decoration: none;">"</del><ref>Solomonoffm, R. J. An Inductive Reference Machine. Technical Research Group, New York City. http://world.std.com/~rjs/indinf56.pdf</ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled <ins style="font-weight: bold; text-decoration: none;">''</ins>An Inductive Inference Machine<ins style="font-weight: bold; text-decoration: none;">''</ins>.<ref>Solomonoffm, R. J. An Inductive Reference Machine. Technical Research Group, New York City. http://world.std.com/~rjs/indinf56.pdf</ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4656&oldid=prevCaseorganic at 21:21, 2 July 20112011-07-02T21:21:55Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:21, 2 July 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning. McGraw Hill, 1997. Pg. 2.</ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images. <ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf</ref></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images.<ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf</ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td></tr>
</table>Caseorganichttps://cyborganthropology.com/index.php?title=Machine_Learning&diff=4655&oldid=prevCaseorganic at 21:21, 2 July 20112011-07-02T21:21:44Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:21, 2 July 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Machine Learning is a process of training a computer algorithm to properly classify future inputs after having trained the algorithm with sample data. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T<del style="font-weight: bold; text-decoration: none;">. (1997)</del>. Machine Learning<del style="font-weight: bold; text-decoration: none;">, </del>McGraw Hill. <del style="font-weight: bold; text-decoration: none;">ISBN 0-07-042807-7, p</del>.2.</ref></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Tom M. Mitchell provided a widely quoted definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."<ref>Mitchell, T. Machine Learning<ins style="font-weight: bold; text-decoration: none;">. </ins>McGraw Hill<ins style="font-weight: bold; text-decoration: none;">, 1997</ins>. <ins style="font-weight: bold; text-decoration: none;">Pg</ins>. 2.</ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images. <ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A program is first trained with known inputs, and "learns" these patterns through one or more statistical methods. Then it can classify new input based on the inputs it has seen before. Machine Learning is often used in computer vision to classify images. <ref>Machine Learning in Computer Vision. Ajay Joshi, Anoop Cherian and Ravishankar Shivalingam. Dept. of Computer Science, University of Minnesota http://www-users.cs.umn.edu/~cherian/ppt/MachineLearningTut.pdf</ref></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===History===</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled "An Inductive Inference Machine."<ref>http://world.std.com/~rjs/indinf56.pdf</ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Ray Solomonoff published the first report on non-semantic machine learning in 1956, titled "An Inductive Inference Machine."<ref><ins style="font-weight: bold; text-decoration: none;">Solomonoffm, R. J. An Inductive Reference Machine. Technical Research Group, New York City. </ins>http://world.std.com/~rjs/indinf56.pdf</ref> He also was a pioneer in algorithmic probability, publishing several papers in the 1960s. It wasn't until the late 1970s that machine learning started emerging as a more focused field of study in computer vision. Machine learning has also been applied in many other fields such as [[Natural Language Processing|natural language processing]], speech and handwriting recognition, and sentiment analysis. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Examples===</div></td></tr>
</table>Caseorganic