Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 58
... sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these ...
... sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these ...
Sivu 61
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = N Σ X ; 1 ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN } ) is then given by ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = N Σ X ; 1 ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN } ) is then given by ...
Sivu 123
... mean of a distribution . The sample mean or center of gravity of a set of points usually serves as a good estimate for the mean of the probability distribution of which the points are samples . It is true that if the probability ...
... mean of a distribution . The sample mean or center of gravity of a set of points usually serves as a good estimate for the mean of the probability distribution of which the points are samples . It is true that if the probability ...
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 |