Changes

[[FILE:Normal Distribution PDF.svg|border|right|320px|thumb|A set of Gaussians]]* Non standard approach relied on a [https://en.wikipedia.org/wiki/Mixture_model mixture model] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=66266&start=7 Re: Winter Released] by [[Jonathan Rosenthal]], [[CCC]], January 09, 2018</ref>, and since Winter '''0.3''' on [https://en.wikipedia.org/wiki/Fuzzy_clustering #Fuzzy_C-means_clustering Fuzzy C-Means], a more direct generalization of a [[Tapered Eval|tapered eval]] with disjoint phases aka clusters <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=69288 Winter 0.3 Release Overview and Select Games] by [[Jonathan Rosenthal]], [[CCC]], December 16, 2018</ref> <ref>[[Mathematician#JCBezdek|James C. Bezdek]], [http://www.legacy.com/obituaries/saltlaketribune/obituary.aspx?n=robert-ehrlich&pid=189574728 Robert Ehrlich], [https://www.researchgate.net/profile/William_Full William Full] ('''1984'''). ''FCM: The fuzzy c-means clustering algorithm''. [https://www.journals.elsevier.com/computers-and-geosciences Computers & Geosciences], Vol. 10, Nos. 2-3, [https://pdfs.semanticscholar.org/64a8/77d135db3acbc23c295367927176f332595f.pdf pdf]</ref>** 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 Wikipediaclusters]</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 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 ** For each Gaussian cluster, a separate evaluation function is trained. When the evaluation function is called the relative probability a position stems from each Gaussian cluster is estimated, the evaluation functions are computed and the final score is returned as the weighted average - a generalization of [[Tapered Eval|tapered eval]] with [[Game Phases|game phases]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=65466&start=4 Re: Tapered Eval between 4 phases] by [[Jonathan Rosenthal]], [[CCC]], October 16, 2017</ref>* Parameter weights are trained via a mixture of [[Reinforcement Learning|reinforcement ]] ([[Temporal Difference Learning|temporal difference]]) learning and [[Supervised Learning|supervised learning]]** Minimizing the [https://en.wikipedia.org/wiki/Cross_entropy cross entropy] loss of a [[Automated Tuning#LogisticRegression|Logistic Regression]] model for each of the k Gaussiansphase