Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced ...
... selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced ...
Sivu 4
... selection is a pressing one . Unfortunately , there is very little theory to guide our selection of measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an ...
... selection is a pressing one . Unfortunately , there is very little theory to guide our selection of measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an ...
Sivu 8
... selection might be made . · 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways . Sometimes they are calculated with precision on the basis of complete a priori knowl- edge about the ...
... selection might be made . · 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways . Sometimes they are calculated with precision on the basis of complete a priori knowl- edge about the ...
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 |