Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 58
... sample mean ( or center of gravity ) of the ith category , and ( Σ ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( Σ ) ; are reasonable * estimates of M ; and ; , respectively . The use of these ...
... sample mean ( or center of gravity ) of the ith category , and ( Σ ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( Σ ) ; are reasonable * estimates of M ; and ; , respectively . The use of these ...
Sivu 60
... mean of X from u to u1 , น and the covariance matrix from + K to + K1 . Training the classi- fying machine on the pattern X1 is accomplished by modifying the dis- criminant ... sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
... mean of X from u to u1 , น and the covariance matrix from + K to + K1 . Training the classi- fying machine on the pattern X1 is accomplished by modifying the dis- criminant ... sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
Sivu 61
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |