Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 76
Sivu ix
CONTENTS I Preface , vii TRAINABLE PATTERN CLASSIFIERS 1.1 Machine classification of data , 1 1.2 The basic model , 2 1.3 The problem of what to measure , 4 1.4 Decision surfaces in pattern space , 4 1.5 Discriminant functions , 6 1.6 ...
CONTENTS I Preface , vii TRAINABLE PATTERN CLASSIFIERS 1.1 Machine classification of data , 1 1.2 The basic model , 2 1.3 The problem of what to measure , 4 1.4 Decision surfaces in pattern space , 4 1.5 Discriminant functions , 6 1.6 ...
Sivu 7
discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
Sivu 16
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
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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 |