Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 13
Sivu 8
The sign of g ( x ) can be evaluated by a threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of ...
The sign of g ( x ) can be evaluated by a threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of ...
Sivu 21
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) 0 . W Discriminant d x2 g ( x ) Σ Wir ii i = 1 + w d +1 : W ; X ; LA +1 or - -1 Response Summing device Threshold element X : w 1 1 nd ...
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) 0 . W Discriminant d x2 g ( x ) Σ Wir ii i = 1 + w d +1 : W ; X ; LA +1 or - -1 Response Summing device Threshold element X : w 1 1 nd ...
Sivu 128
Moreover , T can always be selected so that the first p , diagonal elements of A are positive , the next pz diagonal elements are negative , and the last d - ( pı + p2 ) diagonal elements p1 are zero , where d is the order of A ...
Moreover , T can always be selected so that the first p , diagonal elements of A are positive , the next pz diagonal elements are negative , and the last d - ( pı + p2 ) diagonal elements p1 are zero , where d is the order of A ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
1 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 covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |