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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ Wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |