Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 115
... machines was illustrated in Fig . 2.6 . A PWL machine consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine ...
... machines was illustrated in Fig . 2.6 . A PWL machine consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine ...
Sivu 116
... PWL machine with L ; linear discrimi- nators in the Lith bank might be an appropriate pattern classifier . Pattern - classifying tasks that have many different prototype pat- terns per category are common . The weather - prediction ...
... PWL machine with L ; linear discrimi- nators in the Lith bank might be an appropriate pattern classifier . Pattern - classifying tasks that have many different prototype pat- terns per category are common . The weather - prediction ...
Sivu 122
... PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence of patterns from the training set . In the next section we ...
... PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence of patterns from the training set . In the next section we ...
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 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 |