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 1 . SY = Y , Y , · Yks · Each Y in Sy is a member of y . k 2. Every element ...
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 1 . SY = Y , Y , · Yks · Each Y in Sy is a member of y . k 2. Every element ...
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 ...
Sivu 93
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wk + 1 . They also show ...
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wk + 1 . They also show ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |