Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 18
Sivu 80
... training problem for a two - category linear machine given train- ing subsets Yi and Y2 is to find a W such that inequalities ( 5.1 ) are satisfied . We shall solve this problem by generating a sequence of weight Wk , . . . such that ...
... training problem for a two - category linear machine given train- ing subsets Yi and Y2 is to find a W such that inequalities ( 5.1 ) are satisfied . We shall solve this problem by generating a sequence of weight Wk , . . . such that ...
Sivu 90
... 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 respective kth mem ...
... 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 respective kth mem ...
Sivu 91
... training patterns belonging to category 2 ) . We desire to find a solution weight vector W such that W.Y > 0 for ... training sequence on y ' . The fractional correction rule generates a weight - vector sequence Sw as follows : Begin ...
... training patterns belonging to category 2 ) . We desire to find a solution weight vector W such that W.Y > 0 for ... training sequence on y ' . The fractional correction rule generates a weight - vector sequence Sw as follows : Begin ...
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 |