Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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lying the model , namely that the data to be classified consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention ...
lying the model , namely that the data to be classified consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention ...
Sivu 115
A PWL machine consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary ...
A PWL machine consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary ...
Sivu 128
... App , are the next p2 diagonal elements of A. 1 Now let T1 be a dX p1 matrix consisting of the first p1 columns of T , and let T2 be a dX p2 matrix consisting of the next p2 columns of T. Be- cause T is orthogonal we can write Eq ...
... App , are the next p2 diagonal elements of A. 1 Now let T1 be a dX p1 matrix consisting of the first p1 columns of T , and let T2 be a dX p2 matrix consisting of the next p2 columns of T. Be- cause T is orthogonal we can write Eq ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |