Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 85
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 ...
... 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 ...
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 |