Supervised Learning

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Supervised Learning, (SL)
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. 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 [1].

SL in a nutshell

Supervised machine learning in a nutshell.svg

[2]

SL in Chess

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 can be applied by linear regression to minimize a cost function considering the rank-number of the desired move in a move list ordered by score [3].

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

Selected Publications

1960 ....

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

1980 ...

1990 ...

2000 ...

2010 ...

Forum Posts

External Links

References

  1. Supervised learning from Wikipedia
  2. A data flow diagram shows the machine learning process in summary, by EpochFail, November 15, 2015, Wikimedia Commons
  3. Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
  4. No free lunch in search and optimization - Wikipedia
  5. Occam's razor from Wikipedia

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