Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 9
Sivu 80
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , " Yk , 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine given ...
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , " Yk , 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine given ...
Sivu 90
... Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, ... , SŴR . Let V be the kth member of the sequence Sy . If the ...
... Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, ... , SŴR . Let V be the kth member of the sequence Sy . If the ...
Sivu 91
... sequence those patterns Y ' for which W Y ' > 0. The reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of ...
... sequence those patterns Y ' for which W Y ' > 0. The reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of ...
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 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 |