Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
Sivu 62
... derivation is given by Minsky . Winder has determined that the weights specified by Eqs . ( 3 · 14 ) and ( 3 ∙ 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests another ...
... derivation is given by Minsky . Winder has determined that the weights specified by Eqs . ( 3 · 14 ) and ( 3 ∙ 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests another ...
Sivu 77
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4-3 stem from a variety of sources . The fixed - increment and absolute ...
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4-3 stem from a variety of sources . The fixed - increment and absolute ...
Sivu 109
... Derivation of a discriminant function for a layered machine It was mentioned in Sec . 6.1 that the discriminant functions of layered machines are piecewise linear . In this section , we shall verify this statement . Consider the first ...
... Derivation of a discriminant function for a layered machine It was mentioned in Sec . 6.1 that the discriminant functions of layered machines are piecewise linear . In this section , we shall verify this statement . Consider the first ...
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 |