Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
Sivu 110
... binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each +1 or 1 , depending on whether Y. W ; is greater than or ...
... binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each +1 or 1 , depending on whether Y. W ; is greater than or ...
Sivu 111
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
Sivu 136
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
5 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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |