Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 48
Sivu 5
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
Sivu 16
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
Sivu 24
... discriminant function , is given by an expression of the form g . ) ( X ) = WilX1 + Wi2x2 + + WidXa + wi , d + 1 ( 2 · 19 ) X ... w . Discriminator 1 ( 1 ) il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
... discriminant function , is given by an expression of the form g . ) ( X ) = WilX1 + Wi2x2 + + WidXa + wi , d + 1 ( 2 · 19 ) X ... w . Discriminator 1 ( 1 ) il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
Sisältö
Preface vii | 11 |
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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |