Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 12
Miller11 illustrates a method for selecting a small number of " good " measurements from a larger pool of measurements . Block , Nilsson , and Duda12 describe a method for determining features of pat- terns . Some specific ...
Miller11 illustrates a method for selecting a small number of " good " measurements from a larger pool of measurements . Block , Nilsson , and Duda12 describe a method for determining features of pat- terns . Some specific ...
Sivu 30
That is , the parameters which determine a 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 .
That is , the parameters which determine a 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 .
Sivu 45
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 the machine always assigned this X to category io .
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 the machine always assigned this X to category io .
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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 |