Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 28
Sivu 26
... piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum ...
... piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum ...
Sivu 112
... linear combi- nation of the functions f1 , ... , fp . Since each of the fi , . . . , fp is a linear function of the ... piecewise linear machine . A layered machine with P TLUs in the first layer has a total of 2P linear subsidiary ...
... linear combi- nation of the functions f1 , ... , fp . Since each of the fi , . . . , fp is a linear function of the ... piecewise linear machine . A layered machine with P TLUs in the first layer has a total of 2P linear subsidiary ...
Sivu 134
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for for a hypersphere , 38 functions , 30 for a quadric ...
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for for a hypersphere , 38 functions , 30 for a quadric ...
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 |