Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... theory is not yet generally appreciated by many researchers in the computer - related fields . Con- tributions to this theory have come from many disciplines including statistics , switching theory , physiological psychology , and ...
... theory is not yet generally appreciated by many researchers in the computer - related fields . Con- tributions to this theory have come from many disciplines including statistics , switching theory , physiological psychology , and ...
Sivu 44
... theory . Statistical decision theory can be used as a means to establish the dis- criminant functions for probabilistic patterns governed by known proba- bility functions . Central to the decision - theoretic treatment is the specifi ...
... theory . Statistical decision theory can be used as a means to establish the dis- criminant functions for probabilistic patterns governed by known proba- bility functions . Central to the decision - theoretic treatment is the specifi ...
Sivu 126
... Theory , vol . 178 , no . 5 , pp . S82 - S91 , September , 1962 . 5 Firschein , O. , and M. Fischler : Automatic Subclass Determination for Pattern- recognition Applications , Trans . IEEE on Elect . Computers , vol . EC - 12 , no . 2 ...
... Theory , vol . 178 , no . 5 , pp . S82 - S91 , September , 1962 . 5 Firschein , O. , and M. Fischler : Automatic Subclass Determination for Pattern- recognition Applications , Trans . IEEE on Elect . Computers , vol . EC - 12 , no . 2 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |