Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 84
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
Sivu 85
... solution region W exists , then clearly each W in W can be scaled such that its dot product with each of the members of y ' is greater than any arbitrary positive constant . Let W ' be the region of solution weight vectors , lying in ...
... solution region W exists , then clearly each W in W can be scaled such that its dot product with each of the members of y ' is greater than any arbitrary positive constant . Let W ' be the region of solution weight vectors , lying in ...
Sivu 89
... solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional ... solution weight vectors W1 , W2 , ... , Wr . = The next step in the proof is to form from the reduced training sequence Sy ...
... solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional ... solution weight vectors W1 , W2 , ... , Wr . = The next step in the proof is to form from the reduced training sequence Sy ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |