Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 92
that if 0 < ≤ 2 , then - WIIW - k WI ( 5.38 ) k for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. - k For ...
that if 0 < ≤ 2 , then - WIIW - k WI ( 5.38 ) k for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. - k For ...
Sivu 93
Block3 has proved a generalized version of Theo- rem 5.1 in which the correction increment ck of Eq . ( 5-4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
Block3 has proved a generalized version of Theo- rem 5.1 in which the correction increment ck of Eq . ( 5-4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
Sivu 113
Each has one layer of trainable TLUs ; they differ only in which layer is trained . Machines of this type have been studied by Widrow3 ( who calls them MADALINES for multiple ADALINES ) and by Kaylor . " Efron ' proved that the fixed ...
Each has one layer of trainable TLUs ; they differ only in which layer is trained . Machines of this type have been studied by Widrow3 ( who calls them MADALINES for multiple ADALINES ) and by Kaylor . " Efron ' proved that the fixed ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |