Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 38
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 120
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
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 |