Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 7
Sivu 115
... MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general form for such machines was illustrated in Fig . 2.6 . A PWL machine consists of R discriminators ...
... MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general form for such machines was illustrated in Fig . 2.6 . A PWL machine consists of R discriminators ...
Sivu 122
... PWL machines To apply the closest - mode decision method , we need a training procedure to locate the modes or centers of high pattern density . Suppose that we have a PWL machine whose subsidiary discriminant functions are repre ...
... PWL machines To apply the closest - mode decision method , we need a training procedure to locate the modes or centers of high pattern density . Suppose that we have a PWL machine whose subsidiary discriminant functions are repre ...
Sivu 123
... PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training ...
... PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training ...
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 |