Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of ...
Sivu 89
... training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as ... sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z ...
... training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as ... sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z ...
Sivu 90
... sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth ...
... sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth ...
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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |