Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 56
... covariance matrices For the optimum discriminant functions for normal patterns , expansion of Eq . ( 3-31 ) yields gi ( X ) = = -1 / 1⁄2X'Σ ; -1X + X'2 , -1M ; pi 1/2 i - 1⁄2MΣ ; 1M ; + log p ; - 1⁄2 log i = 1 , R ( 3-32 ) " In the ...
... covariance matrices For the optimum discriminant functions for normal patterns , expansion of Eq . ( 3-31 ) yields gi ( X ) = = -1 / 1⁄2X'Σ ; -1X + X'2 , -1M ; pi 1/2 i - 1⁄2MΣ ; 1M ; + log p ; - 1⁄2 log i = 1 , R ( 3-32 ) " In the ...
Sivu 58
... covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i ...
... covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i ...
Sivu 59
... covariance matrices , we can derive a training process which makes optimum use of the set of training patterns . In this section we shall illustrate this derivation for the case in which the covariance matrices are all known but for ...
... covariance matrices , we can derive a training process which makes optimum use of the set of training patterns . In this section we shall illustrate this derivation for the case in which the covariance matrices are all known but for ...
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 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 |