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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |