Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
Sivu 76
From Chapter 2 we recall that a machine can be implemented by a processor followed by a linear machine . The processor converts the set X of d - dimensional pattern vectors into a set F of M - dimensional vectors by the mapping F = F ...
From Chapter 2 we recall that a machine can be implemented by a processor followed by a linear machine . The processor converts the set X of d - dimensional pattern vectors into a set F of M - dimensional vectors by the mapping F = F ...
Sivu 129
The implementation shown in Fig . A ∙ 1 is a quadric discriminator containing adjustable weights and coefficients that can be set according to Eqs . ( A · 7 ) and ( A - 8 ) . The coefficients follow- ing the squarers are set at plus ...
The implementation shown in Fig . A ∙ 1 is a quadric discriminator containing adjustable weights and coefficients that can be set according to Eqs . ( A · 7 ) and ( A - 8 ) . The coefficients follow- ing the squarers are set at plus ...
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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 |