Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 35
Sivu 9
... number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications of these patterns are assumed to be known . Discriminant ...
... number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications of these patterns are assumed to be known . Discriminant ...
Sivu 32
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
Sivu 121
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
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 |