Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 57
... belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , .. , XR , where X is the training subset of all patterns belonging to category i . These subsets can be used to estimate ; and M ...
... belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , .. , XR , where X is the training subset of all patterns belonging to category i . These subsets can be used to estimate ; and M ...
Sivu 75
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every ...
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every ...
Sivu 121
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. j Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category having ...
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. j Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category having ...
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 |