Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 4
... problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical situation ...
... problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical situation ...
Sivu 12
... problems . The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , " and Marill and Green 10 propose and discuss tests for the " effectiveness " of measurements ...
... problems . The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , " and Marill and Green 10 propose and discuss tests for the " effectiveness " of measurements ...
Sivu 89
... problem as a dichotomy problem in a higher - dimensional space and then applying Theorem 5.1 . The first step is to generate a new set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ...
... problem as a dichotomy problem in a higher - dimensional space and then applying Theorem 5.1 . The first step is to generate a new set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ...
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 |