Changes

Jump to: navigation, search

Supervised Learning

580 bytes added, 23:33, 4 February 2020
no edit summary
'''[[Main Page|Home]] * [[Learning]] * Supervised Learning'''
[[FILE:'''Supervised machine Learning''', (SL)<br/>is learning in from examples provided by a nutshellknowledgable external [https://en.wikipedia.org/wiki/Supervisor supervisor].svg|480px|border|right|thumb| Supervised Learning <ref>A data flow diagram shows the In machine learning process in summary, by supervised learning is a technique for deducing a function from [https://en.wikipedia.org/wiki/User:EpochFail EpochFail]Training, November 15_validation, 2015_and_test_sets training data]. The training data consist of pairs of input objects and desired outputs. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set <ref>[https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia CommonsSupervised_learning Supervised learning from Wikipedia]</ref> ]] .
'''=SL in a nutshell=[[FILE:Supervised Learning''',<br/>is machine learning from examples provided by in a knowledgable external [https://ennutshell.wikipedia.org/wiki/Supervisor supervisorsvg|640px|none|border|text-bottom]]. In <ref>A data flow diagram shows the machine learningprocess in summary, supervised learning is a technique for deducing a function from by [https://en.wikipedia.org/wiki/TrainingUser:EpochFail EpochFail],_validationNovember 15,_and_test_sets training data]. The training data consist of pairs of input objects and desired outputs. After parameter adjustment and learning2015, the performance of the resulting function should be measured on a test set that is separate from the training set <ref>[https://en.wikipedia.org/wiki/Supervised_learning Supervised learning from WikipediaWikimedia_Commons Wikimedia Commons]</ref>.  =SL in Chess=In computer games and chess, supervised learning techniques were used in [[Automated Tuning|automated tuning]] or to train [[Neural Networks|neural network]] game and chess programs. Input objects are [[Chess Position|chess positions]]. The desired output is either the supervisor's move choice in that position ([[Automated Tuning#MoveAdaption|move adaption]]), or a [[Score|score]] provided by an [[Oracle|oracle]] ([[Automated Tuning#ValueAdaption|value adaption]]).
==Move Adaption==
[[Automated Tuning#MoveAdaption|Move adaption]] can be applied by [[Automated Tuning#LinearRegression|linear regression]] to minimize a [https://en.wikipedia.org/wiki/Loss_function cost function] considering the rank-number of the desired move in a [[Move List|move list]] ordered by score <ref>[[Tony Marsland]] ('''1985'''). ''Evaluation-Function Factors''. [[ICGA Journal#8_2|ICCA Journal, Vol. 8, No. 2]], [http://webdocs.cs.ualberta.ca/~tony/OldPapers/evaluation.pdf pdf]</ref>.
==Value Adaption==
One common idea to provide an [[Oracle|oracle]] for supervised [[Automated Tuning#ValueAdaption|value adaption]] is to use the win/draw/loss outcome from finished games
for all training positions selected from that game. Discrete {-1, 0, +1} or {0, ½, 1} desired values are the domain of [[Automated Tuning#LogisticRegression|logistic regression]] and require the
* [[Dave Gomboc]], [[Michael Buro]], [[Tony Marsland]] ('''2005'''). ''Tuning Evaluation Functions by Maximizing Concordance''. [https://en.wikipedia.org/wiki/Theoretical_Computer_Science_%28journal%29 Theoretical Computer Science], Vol. 349, No. 2, [http://www.cs.ualberta.ca/%7Emburo/ps/tcs-learn.pdf pdf]
* [[Amos Storkey]], [https://www.k.u-tokyo.ac.jp/pros-e/person/masashi_sugiyama/masashi_sugiyama.htm Masashi Sugiyama] ('''2006'''). ''[http://papers.neurips.cc/paper/3019-mixture-regression-for-covariate-shift Mixture Regression for Covariate Shift]''. [https://dblp.uni-trier.de/db/conf/nips/nips2006.html NIPS 2006]
* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2008'''). ''Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization''. [http://www.sigevo.org/gecco-2008/ GECCO '08], [https://arxiv.org/abs/1711.06839 arXiv:1711.06839]
* [[Eli David|Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2009'''). ''Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions''. [http://www.sigevo.org/gecco-2009/ GECCO '09], [https://arxiv.org/abs/1711.06840 arXiv:1711.06840]
==2010 ...==
* [[Tor Lattimore]], [[Marcus Hutter]] ('''2011'''). ''No Free Lunch versus Occam's Razor in Supervised Learning''. [https://en.wikipedia.org/wiki/Ray_Solomonoff Solomonoff] Memorial, [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], [https://en.wikipedia.org/wiki/Springer-Verlag Springer], [https://arxiv.org/abs/1111.3846 arXiv:1111.3846] <ref>[https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization No free lunch in search and optimization - Wikipedia]</ref> <ref>[https://en.wikipedia.org/wiki/Occam%27s_razor Occam's razor from Wikipedia]</ref>
Retrieved from "https://www.chessprogramming.org/Special:MobileDiff/15518...17870"

Navigation menu