Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 70
... presented for trial . Suppose that the TLU with present weight vector W responds incorrectly to an augmented pattern ... presentation of each pattern is repeated until the pattern is categorized correctly . • In another variation , it is ...
... presented for trial . Suppose that the TLU with present weight vector W responds incorrectly to an augmented pattern ... presentation of each pattern is repeated until the pattern is categorized correctly . • In another variation , it is ...
Sivu 125
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
Sivu 126
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
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 |