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 , 2 - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi1⁄2M ; ' Σ - 1M ; If R = 2 , and if 21 = Σε = Σ , then the single discriminant ...
... given by the values of the components of the transformed mean vector , 2 - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , log pi1⁄2M ; ' Σ - 1M ; If R = 2 , and if 21 = Σε = Σ , then the single discriminant ...
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 P. ) j = 1 , L. That is , there are L1 typical patterns belonging ...
... 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 P. ) j = 1 , L. That is , there are L1 typical patterns belonging ...
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 |