Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 58
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 , If R = 2 , and if Σ1 g ( X ) can be written as g ( X ) = log pi1⁄2M'Σ - 1M ; = Σε - = Σ ...
... 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 , If R = 2 , and if Σ1 g ( X ) can be written as g ( X ) = log pi1⁄2M'Σ - 1M ; = Σε - = Σ ...
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 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 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 |