'''[[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
