Pattern Recognition and Machine Learning
This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.
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Recognition and Learning by a Computer
Generation and Transformation
Pattern Feature Extraction
Pattern Understanding Methods
Learning Based on Logic
algorithm amount of exhaust Answer Figure assume attribute Boltzmann machine boundary line called Chapter classified cluster color competitive learning constant symbols constraint corresponds decision tree define definition delete described in Section directed graph domain edge ErrorFunc Euclidean space example executed explain feature extraction Fourier given goal concept Hopfield network Horn clause inference rule initial input pattern knowledge label let us consider line drawing literals maximal partial match neural network object obtain operators optical flow output unit pair parameter pattern element pattern function pattern recognition pattern understanding positive instance predicate logic printf problem solving production system proof tree quantized recognition and learning region relation relaxation method room2 rooml semantic network shown in Figure solid structure subgoal subgraph substitution Suppose th letter transformation triangle triangular table variable symbols vector version space vertex weights well-formed formula