Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 9
... cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The ...
... cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The ...
Sivu 52
... cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3 · 3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging ...
... cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3 · 3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging ...
Sivu 122
... cluster of like - category patterns will be called a mode - seeking training method . ( We assume that the weight vectors in the ith bank are ad- justed toward the centers of clusters belonging to category i only for all i 1 , . . . , R ...
... cluster of like - category patterns will be called a mode - seeking training method . ( We assume that the weight vectors in the ith bank are ad- justed toward the centers of clusters belonging to category i only for all i 1 , . . . , R ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |