Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 8
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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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Then , a PWL machine with L ; linear discrimi- nators in the Lith bank might be an appropriate pattern classifier . Pattern - classifying tasks that have many different prototype pat- terns per category are common .
Then , a PWL machine with L ; linear discrimi- nators in the Lith bank might be an appropriate pattern classifier . Pattern - classifying tasks that have many different prototype pat- terns per category are common .
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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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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 |