Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 69
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
Sivu 75
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
Sivu 123
... presented because it illustrates several that have been proposed for mode seeking . No rigorous theo- retical treatment has been advanced to support it , and only limited em- pirical evidence has been collected to justify its use , but ...
... presented because it illustrates several that have been proposed for mode seeking . No rigorous theo- retical treatment has been advanced to support it , and only limited em- pirical evidence has been collected to justify its use , but ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |