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 . 8 9 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 . 8 9 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 called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |