Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 65
... training set X of N patterns and that x is divided into two training subsets Xi and X2 . The subset X1 contains those patterns belonging to category 1 , and X2 contains those patterns belonging to category 2. In this chapter we shall ...
... training set X of N patterns and that x is divided into two training subsets Xi and X2 . The subset X1 contains those patterns belonging to category 1 , and X2 contains those patterns belonging to category 2. In this chapter we shall ...
Sivu 119
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
Sivu 120
... training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does ...
... training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are normal.3 There does ...
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 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 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 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 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 |