Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 27
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 , , fr . Since each of the fi , . . . , fp is a linear function of the pattern vector , the subsidiary discriminant func- tions are also linear . Thus ... piecewise linear 112 LAYERED MACHINES.
... linear combi- nation of the functions f1 , , fr . Since each of the fi , . . . , fp is a linear function of the pattern vector , the subsidiary discriminant func- tions are also linear . Thus ... piecewise linear 112 LAYERED MACHINES.
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 a hypersphere , 38 for 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 a hypersphere , 38 for 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 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 Stanford step subsidiary discriminant Suppose 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 |