Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 16
Sivu 82
... reduced · · " training sequence by the symbol Sf . k , · . . In Sŵ , Ŵ1 = If we now begin with the same initial weight vector W1 as before and apply the fixed - increment error - correction rule with the reduced training sequence , we ...
... reduced · · " training sequence by the symbol Sf . k , · . . In Sŵ , Ŵ1 = If we now begin with the same initial weight vector W1 as before and apply the fixed - increment error - correction rule with the reduced training sequence , we ...
Sivu 89
... reduced training sequence Sp and the reduced weight - vector sequences Sŵ ,, . . . , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth ...
... reduced training sequence Sp and the reduced weight - vector sequences Sŵ ,, . . . , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth ...
Sivu 91
... reduced training sequence Sy then creates a reduced weight - vector sequence S✩ such that • k k ( 5.37 ) for all Ŷ in Sy and for all Ŵ in Sŵ . k Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced ...
... reduced training sequence Sy then creates a reduced weight - vector sequence S✩ such that • k k ( 5.37 ) for all Ŷ in Sy and for all Ŵ in Sŵ . k Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced ...
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 |