Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 28
Sivu 57
... belonging to a single category is a hyper- spherical cluster and each ... patterns , a quadric machine , does not depend on the values of the ... patterns in PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
... belonging to a single category is a hyper- spherical cluster and each ... patterns , a quadric machine , does not depend on the values of the ... patterns in PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
Sivu 75
... patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and ... patterns divided into subsets Y1 , Y2 , . . . , YR which are linearly separable . The subset y ; con- tains all ...
... patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and ... patterns divided into subsets Y1 , Y2 , . . . , YR which are linearly separable . The subset y ; con- tains all ...
Sivu 121
... patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The ... belonging to cate- gory 1 , L2 belonging to category 2 , etc. 9 Then , given these modes , one reasonable way ...
... patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The ... belonging to cate- gory 1 , L2 belonging to category 2 , etc. 9 Then , given these modes , one reasonable way ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying 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 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 |