Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 5
... denote both the pattern point and the pattern vector by the symbol X. · A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of x2 R3 R 2 R - The ...
... denote both the pattern point and the pattern vector by the symbol X. · A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of x2 R3 R 2 R - The ...
Sivu 110
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each +1 or 1 , depending on whether Y. W ; is greater than or less than zero . Let us denote the dot products Y ...
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each +1 or 1 , depending on whether Y. W ; is greater than or less than zero . Let us denote the dot products Y ...
Sivu 123
... denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector wi [ k + 1 ] ...
... denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector wi [ k + 1 ] ...
Sisältö
Preface vii | 11 |
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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |