Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 15
Sivu 82
... proof of Theorem 5.1 is further simplified if we omit from the training sequence Sy any patterns Y ' for which W + 1 ... Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector ...
... proof of Theorem 5.1 is further simplified if we omit from the training sequence Sy any patterns Y ' for which W + 1 ... Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector ...
Sivu 89
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying ...
Sivu 92
... proof of Theorem 5.1 was outlined by Rosenblatt.1 Subsequent proofs have been given by Joseph , 2 Block , Charnes , Novikoff , 5 Single- ton , Ridgway , ' and possibly others . Our Proof 1 follows the method 6 * Since Sŵ terminates when ...
... proof of Theorem 5.1 was outlined by Rosenblatt.1 Subsequent proofs have been given by Joseph , 2 Block , Charnes , Novikoff , 5 Single- ton , Ridgway , ' and possibly others . Our Proof 1 follows the method 6 * Since Sŵ terminates when ...
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 |