Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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CHAPTER 7 PIECEWISE LINEAR MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general form for such machines was illustrated in Fig . 2.6 .
CHAPTER 7 PIECEWISE LINEAR MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general form for such machines was illustrated in Fig . 2.6 .
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What is needed to apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one ...
What is needed to apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one ...
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Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step .
Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step .
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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 |