Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 12
... specific examples of measurement devices for optical charac- ter recognition are discussed in a book edited by Fischer et al.13 Reports by Brain et al . 14 discuss the development of " optical preprocessors " for visual data ...
... specific examples of measurement devices for optical charac- ter recognition are discussed in a book edited by Fischer et al.13 Reports by Brain et al . 14 discuss the development of " optical preprocessors " for visual data ...
Sivu 30
... specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this property . We shall call the members of these ...
... specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this property . We shall call the members of these ...
Sivu 45
... specific X and for all possible values of i ( i = 1 ,. . . , R ) . Suppose for some specific X , Lx ( i ) is a minimum for iio . That is , Lx ( io ) < Lx ( i ) for i = 1 , . . . , R. We ≤ would minimize the conditional average loss if ...
... specific X and for all possible values of i ( i = 1 ,. . . , R ) . Suppose for some specific X , Lx ( i ) is a minimum for iio . That is , Lx ( io ) < Lx ( i ) for i = 1 , . . . , R. We ≤ would minimize the conditional average loss if ...
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 |