Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 36
Sivu 9
... number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications of these patterns are assumed to be known . Discriminant ...
... number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired classifications of these patterns are assumed to be known . Discriminant ...
Sivu 32
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
Sivu 58
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. where N ; is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( E ) , is called ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. where N ; is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( E ) , is called ...
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 |