Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu vii
... 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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |