Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 55
Sivu 56
... given by the values of the components of the transformed mean vector , Σ - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi - If R = 2 , and if Σ1 = Σε = g ( X ) can be written as M¿'Σ — 1M ; Σ , then the ...
... given by the values of the components of the transformed mean vector , Σ - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi - If R = 2 , and if Σ1 = Σε = g ( X ) can be written as M¿'Σ — 1M ; Σ , then the ...
Sivu 104
... given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUS , we can say that it trans- forms the vertices of I1 ...
... given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUS , we can say that it trans- forms the vertices of I1 ...
Sivu 121
... given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. for i = 1 , . . . , R and j = 1 , . . . , L. That is , there are L1 typical patterns ...
... given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. for i = 1 , . . . , R and j = 1 , . . . , L. That is , there are L1 typical patterns ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |