Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 70
Sivu 9
... training set . The desired classifications of these patterns are assumed to be known . Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the ...
... training set . The desired classifications of these patterns are assumed to be known . Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the ...
Sivu 11
... training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods . On the other hand , employment of decision- theoretic methods ...
... training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods . On the other hand , employment of decision- theoretic methods ...
Sivu 122
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the ...
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the ...
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 |