Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson ... Rain tomorrow 2 3 No rain tomorrow Undecided We shall adopt as our basic model of a pattern classifier a device with d input lines and one output line ( see Fig .
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson ... Rain tomorrow 2 3 No rain tomorrow Undecided We shall adopt as our basic model of a pattern classifier a device with d input lines and one output line ( see Fig .
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Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis- criminators , each of which computes the value of a discriminant function . The outputs of the discriminators will be called discriminants .
Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis- criminators , each of which computes the value of a discriminant function . The outputs of the discriminators will be called discriminants .
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The performance level which the pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training . The training process proceeds as follows : a large number of patterns ...
The performance level which the pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training . The training process proceeds as follows : a large number of patterns ...
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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 |