Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 5
We shall denote both the pattern point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . R. Let the symbol R ; denote the set of " x2 R3 3 .
We shall denote both the pattern point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . R. Let the symbol R ; denote the set of " x2 R3 3 .
Sivu 89
We denote each of the R – 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , • R , ji . 3. Let the ith block of D components of each Z ; | ; ( Y ) be set equal to Y for j = 1 , ... , R , ji .
We denote each of the R – 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , • R , ji . 3. Let the ith block of D components of each Z ; | ; ( Y ) be set equal to Y for j = 1 , ... , R , ji .
Sivu 123
Let us denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector w ...
Let us denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector w ...
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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 |