Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 28
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
Sivu 69
... positive number called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y · W ' will be correctly positive . If W were incorrectly ...
... positive number called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y · W ' will be correctly positive . If W were incorrectly ...
Sivu 100
... positive dot product . That is , a weight vector lying on a pattern hyperplane is assumed to be on the positive side . Other conventions could also have been adopted . weight vectors which are adjusted are those which have dot 100 ...
... positive dot product . That is , a weight vector lying on a pattern hyperplane is assumed to be on the positive side . Other conventions could also have been adopted . weight vectors which are adjusted are those which have dot 100 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |