Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 82
... proof of Theorem 5.1 is further simplified if we omit from the training sequence Sy any patterns Y ' for which W + 1 ... Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector ...
... proof of Theorem 5.1 is further simplified if we omit from the training sequence Sy any patterns Y ' for which W + 1 ... Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector ...
Sivu 84
... 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 ... Proof 2 The following proof of Theorem 5.1 results 84 TRAINING THEOREMS.
... 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 ... Proof 2 The following proof of Theorem 5.1 results 84 TRAINING THEOREMS.
Sivu 134
... proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate ...
... proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate ...
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 |