Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 27
Sivu 101
... adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the ...
... adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the ...
Sivu 103
... adjusted , and W1 and W would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
... adjusted , and W1 and W would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
Sivu 123
... adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the closest one to X + 1 . Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the ...
... adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the closest one to X + 1 . Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |