Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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 R3 R 2 OR ...
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 R3 R 2 OR ...
Sivu 89
We denote each of the R 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , . . . , R , j # i . 3. Let the ith block of D components of each Z. , ( Y ) be set equal to Y for j = 1 , 4 . R , ji .
We denote each of the R 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , . . . , R , j # i . 3. Let the ith block of D components of each Z. , ( Y ) be set equal to Y for j = 1 , 4 . R , ji .
Sivu 106
The arrows denote the positive sides of each line . The resulting image space is shown in Fig . 6.76 . The symbols and O are again used to denote the category associated with each image - point vertex . Note that in this example the two ...
The arrows denote the positive sides of each line . The resulting image space is shown in Fig . 6.76 . The symbols and O are again used to denote the category associated with each image - point vertex . Note that in this example the two ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |