Supervised Learning

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Supervised Learning, is learning from examples provided by a knowledgable external supervisor. In machine learning, supervised learning is a technique for deducing a function from training data. The training data consist of pairs of input objects and desired outputs. In computer games and chess, supervised learning techniques were used in automated tuning or to train neural network game and chess programs. Input objects are chess positions. The desired output is either the supervisor's move choice in that position (move adaption), or a score provided by an oracle (value adaption).

=Move Adaption= Move adaption applies linear regression using a cost function to minimize the rank-number of the desired move in a move list ordered by score.

=Value Adaption= One common idea to provide an oracle for supervised 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 logistic regression and require the evaluation scores mapped from pawn advantage to appropriate winning probabilities using the sigmoid function to calculate a mean squared error of the cost function to minimize, as demonstrated by Texel's Tuning Method.

=See also=
 * Supervised Learning in Automated Tuning
 * Book Learning
 * Chessmaps Heuristic
 * CHREST
 * Deep Learning
 * Neural Networks
 * Planning
 * Reinforcement Learning
 * Temporal Difference Learning

=Selected Publications=

1960 ....

 * Arthur Samuel (1967). Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress. pdf

1980 ...

 * Thomas Nitsche (1982). A Learning Chess Program. Advances in Computer Chess 3
 * Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
 * Eric B. Baum, Frank Wilczek (1987). Supervised Learning of Probability Distributions by Neural Networks. NIPS 1987

1990 ...

 * Michèle Sebag (1990). A symbolic-numerical approach for supervised learning from examples and rules. Ph.D. thesis, Paris Dauphine University
 * Feng-hsiung Hsu, Thomas Anantharaman, Murray Campbell, Andreas Nowatzyk (1990). A Grandmaster Chess Machine. Scientific American, Vol. 263, No. 4
 * Thomas Anantharaman (1997). Evaluation Tuning for Computer Chess: Linear Discriminant Methods. ICCA Journal, Vol. 20, No. 4

2000 ...

 * Michael Buro (2002). Improving Mini-max Search by Supervised Learning. Artificial Intelligence, Vol. 134, No. 1, pdf
 * Dave Gomboc, Michael Buro, Tony Marsland (2005). Tuning Evaluation Functions by Maximizing Concordance. Theoretical Computer Science, Vol. 349, No. 2, pdf

2010 ...

 * Tor Lattimore, Marcus Hutter (2011). No Free Lunch versus Occam's Razor in Supervised Learning. Solomonoff Memorial, Lecture Notes in Computer Science, Springer, arXiv:1111.3846
 * Wen-Jie Tseng, Jr-Chang Chen, I-Chen Wu, Ching-Hua Kuo, Bo-Han Lin (2013). A Supervised Learning Method for Chinese Chess Programs. JSAI2013
 * Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49, pdf
 * Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
 * Wen-Jie Tseng, Jr-Chang Chen, I-Chen Wu, Tinghan Wei (2018). Comparison Training for Computer Chinese Chess. arXiv:1801.07411

=Forum Posts=
 * Re: Insanity... or Tal style? by Miguel A. Ballicora, CCC, April 02, 2009 » Gaviota
 * Re: How Do You Automatically Tune Your Evaluation Tables by Álvaro Begué, CCC, January 08, 2014
 * The texel evaluation function optimization algorithm by Peter Österlund, CCC, January 31, 2014 » Texel's Tuning Method
 * SL vs RL by Chris Whittington, CCC, April 28, 2019

=External Links=
 * Supervised learning from Wikipedia
 * Category: Supervised learning - Scholarpedia
 * Boosting (machine learning) from Wikipedia
 * Computational learning theory from Wikipedia
 * Support vector machine from Wikipedia

=References= Up one Level