Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 52
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables x and x2 is more complicated * than that of Eq ...
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables x and x2 is more complicated * than that of Eq ...
Sivu 70
... zero . In one case , c is taken to be the smallest integer which will make the value of W. Y cross the threshold of zero . That is , we desire that W.Y = ( WcY ) Y > 0 • W'.Y = ( WcY ) · Y < 0 • for W Y erroneously nonpositive and • for ...
... zero . In one case , c is taken to be the smallest integer which will make the value of W. Y cross the threshold of zero . That is , we desire that W.Y = ( WcY ) Y > 0 • W'.Y = ( WcY ) · Y < 0 • for W Y erroneously nonpositive and • for ...
Sivu 100
... zero components . The patterns are arranged in a train- ing sequence and presented to the machine , one at a time ... zero and the machine response will be +1 . Since P is odd , NÂ can never equal zero or be even . We have assumed that ...
... zero components . The patterns are arranged in a train- ing sequence and presented to the machine , one at a time ... zero and the machine response will be +1 . Since P is odd , NÂ can never equal zero or be even . We have assumed that ...
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 |