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 ; , 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 ; , respectively . The use of these ...
Sivu 61
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = Σ X ; ( 3.50 ) the set { X1 , X2 , The optimum a posteriori discriminant function ( after training on , XN ) is then given by Eq . ( 3:31 ) ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = Σ X ; ( 3.50 ) the set { X1 , X2 , The optimum a posteriori discriminant function ( after training on , XN ) is then given by Eq . ( 3:31 ) ...
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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |