Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Suppose the training set consisted of N1 patterns belonging to category 1 and N , patterns belonging to category 2. Reasonable estimates for X , and X , might then be the respective sample means ( centers of gravity ) of the patterns in ...
Suppose the training set consisted of N1 patterns belonging to category 1 and N , patterns belonging to category 2. Reasonable estimates for X , and X , might then be the respective sample means ( centers of gravity ) of the patterns in ...
Sivu 75
4.5 An error - correction training procedure for R > 2 = A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
4.5 An error - correction training procedure for R > 2 = A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
Sivu 121
Suppose the modes for the various categories , as established by a training procedure , are given by the points P.6 . ) for i = 1 , . . . , R and j = 1 , Li . That is , there are Li typical patterns belonging to category 1 ...
Suppose the modes for the various categories , as established by a training procedure , are given by the points P.6 . ) for i = 1 , . . . , R and j = 1 , Li . That is , there are Li typical patterns belonging to category 1 ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |