Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
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 82
... steps ; S✩ will have no repetitions and will therefore terminate at the kōth step if Ŵ + 1 is a weight vector which satisfies inequality ( 5.6 ) . Theorem 5.1 will be proved if we prove that Sŵ terminates . Sŵ = W1 , W2 , • " Ŵ k9 · ko ...
... steps ; S✩ will have no repetitions and will therefore terminate at the kōth step if Ŵ + 1 is a weight vector which satisfies inequality ( 5.6 ) . Theorem 5.1 will be proved if we prove that Sŵ terminates . Sŵ = W1 , W2 , • " Ŵ k9 · ko ...
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 |
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 |