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Pattern Recognition,
is used to assign a label to an input value [1] , for instance to apply classification in machine learning applications, i.e. to identify objects and images, as well as computer chess related pattern of chess positions in Cognitive Psychology and concerning evaluation and control of the search in computer chess. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform a "fuzzy" matching. In contrast, Pattern matching usually has to be exact.
Chess pattern [2]

Chess Pattern

Chess pattern range from simple properties of squares and pieces concerning occupancy and control, to a more complex interrelated sets of features. Recognizers are implemented with decision trees, neural networks, and fuzzy logics. In his ICCA Journal paper Fuzzy Production Rules in Chess, Peter W. Frey [3] proposed feature strings or sets of three types. Type-A features must match completely, type-B feature strings represent features which are usually but not always present, while type-C features are present occasionally but are highly diagnostic when available. Those features were intended to use at the root for an oracle approach.

Chess Programs

See also


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External Links


  1. ^ Pattern recognition from Wikipedia
  2. ^ Clipping from Chess pattern by ~Melmolly on deviantART, Graphics Software by Xara
  3. ^ Peter W. Frey (1986). Fuzzy Production Rules in Chess. ICCA Journal, Vol. 9, No. 4
  4. ^ Ratio Club from Wikipedia
  5. ^ Royal Radar Establishment from Wikipedia
  6. ^ Mikhail Moiseevich Bongard - Михаил Моисеевич Бонгард
  7. ^ Tsumeshogi from Wikipedia
  8. ^ Dennis H. Holding (1992). Theories of Chess Skill. Psychological Research, Vol. 54, No. 1
  9. ^ Marcel van Kervinck (2002). The design and implementation of the Rookie 2.0 Chess Playing Program. Masters Thesis, pdf
  10. ^ Bayesian spam filtering - Wikipedia

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