Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 75
... discussed can be used to train a general linear machine . Suppose we have a set Y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset Y ; con- tains all training patterns in ...
... discussed can be used to train a general linear machine . Suppose we have a set Y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset Y ; con- tains all training patterns in ...
Sivu 77
... discussed in Sec . 4.3 stem from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time ...
... discussed in Sec . 4.3 stem from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time ...
Sivu 118
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
... discussed several nonparametric training methods . Generally , nonparametric training methods are to be preferred to parametric ones because no assumptions need be made about the forms of underlying probability distributions . This ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components 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 step subsidiary discriminant Suppose terns 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 |