Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 28
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic ... DISCRIMINANT FUNCTIONS Quadric decision surfaces, Implementation of quadric discriminant ...
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic ... DISCRIMINANT FUNCTIONS Quadric decision surfaces, Implementation of quadric discriminant ...
Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal ... discriminant functions for PARAMETRIC TRAINING METHODS 55 The optimum classifier for normal patterns,
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal ... discriminant functions for PARAMETRIC TRAINING METHODS 55 The optimum classifier for normal patterns,
Sivu 127
... DISCRIMINANT FUNCTIONS A 1 Separation of a quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column ...
... DISCRIMINANT FUNCTIONS A 1 Separation of a quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 step subsidiary discriminant Suppose terns 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 |