Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 5
... denote both the pattern point and the pattern vector by the symbol X. · • A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R , denote the set of x2 R3 R2 2 OR ...
... denote both the pattern point and the pattern vector by the symbol X. · • A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R , denote the set of x2 R3 R2 2 OR ...
Sivu 110
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W ;. For any given augmented input pattern Y each Ui = +1 or 1 , depending on whether Y W , is greater than or less than zero . Let us denote the dot ...
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W ;. For any given augmented input pattern Y each Ui = +1 or 1 , depending on whether Y W , is greater than or less than zero . Let us denote the dot ...
Sivu 123
... denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector w [ k + 1 ] ...
... denote the weight vector which is to be adjusted at this step by the symbol w [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector w [ k + 1 ] ...
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 |