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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |