Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 75
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two ... error - correction procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when ...
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two ... error - correction procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when ...
Sivu 81
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > W Yk . With this pro ...
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > W Yk . With this pro ...
Sivu 119
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |