Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 20
... subsets X1 , X2 , . . . , XR are linearly separable . Stated another way , a classification of X is linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , GR exist such that 9 XR ...
... subsets X1 , X2 , . . . , XR are linearly separable . Stated another way , a classification of X is linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , GR exist such that 9 XR ...
Sivu 104
... subsets ( N - 1 ) and §2 ( N − 1 ) ̧ Then and only then can the single TLU in the Nth layer produce the de- sired responses for each of the patterns in X1 and X2 . - Given training subsets X1 and X2 , the training problem for layered ...
... subsets ( N - 1 ) and §2 ( N − 1 ) ̧ Then and only then can the single TLU in the Nth layer produce the de- sired responses for each of the patterns in X1 and X2 . - Given training subsets X1 and X2 , the training problem for layered ...
Sivu 120
... subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained ... X1 , X2 , XR and find those k patterns which are closest to X. Suppose that of these k closest patterns ni patterns belong ...
... subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained ... X1 , X2 , XR and find those k patterns which are closest to X. Suppose that of these k closest patterns ni patterns belong ...
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 |