Changes

** The classifier is trained via [[Temporal Difference Learning|temporal difference learning]] to predict whether a [[Moves|move]] will return [[Beta|beta]]

** Classifier considers [[Hash Move|TT move]], [[Killer Move|killers]], [[Moves#Type|move type]], [[Origin Square|from square]], [[Target Square|target square]], [[Captures|capture target]], [[Static Exchange Evaluation|SEE]], target square of last move, [[Check|check]] and changes between forcing and unforcing moves (a capture is more likely after another capture)

* [[Selectivity]]

** [[Null Move Reductions]]

==[[Evaluation]]==

[[FILE:Normal Distribution PDF.svg|border|right|320px|thumb|A set of Gaussians]]

** Assumes [[Chess Position|positions]] encountered in search come from some set of k [https://en.wikipedia.org/wiki/Gaussian_function Gaussians] <ref>[https://en.wikipedia.org/wiki/K-means_clustering k-means clustering from Wikipedia]</ref> <ref>[http://stanford.edu/~cpiech/cs221/handouts/kmeans.html K Means] by [https://web.stanford.edu/~cpiech/bio/index.html Chris Piech]</ref>

** Mixture model is trained via [https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm EM algorithm] <ref>[http://www.ics.uci.edu/~smyth/courses/cs274/notes/EMnotes.pdf The EM Algorithm for Gaussian Mixtures - Probabilistic Learning: Theory and Algorithms, CS 274A] (pdf) [https://en.wikipedia.org/wiki/University_of_California,_Irvine University of California, Irvine]</ref> <ref>[http://people.csail.mit.edu/dsontag/courses/ml12/slides/lecture21.pdf Mixture Models & EM algorithm Lecture 21] (pdf) by [https://people.csail.mit.edu/dsontag/ David Sontag], [https://en.wikipedia.org/wiki/New_York_University New York University]</ref> either on [[Databases|database games]] or positions sampled from search