Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 44
... step 1 . There are some important problems in pattern classification in which these steps can be easily applied . This chapter is devoted to a study of the parametric training method as it is used in these problems . 3.2 Discriminant ...
... step 1 . There are some important problems in pattern classification in which these steps can be easily applied . This chapter is devoted to a study of the parametric training method as it is used in these problems . 3.2 Discriminant ...
Sivu 84
... steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a solution vector W. It should also be pointed out that ...
... steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a solution vector W. It should also be pointed out that ...
Sivu 86
... step must exceed a positive amount bounded away from zero . Because each pattern in y occurs infi- nitely often in the training sequence , steps continue to be made until a weight vector in the solution region is attained , thus proving ...
... step must exceed a positive amount bounded away from zero . Because each pattern in y occurs infi- nitely often in the training sequence , steps continue to be made until a weight vector in the solution region is attained , thus proving ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |