Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 24
Sivu 20
... X2 , Xr are linearly separable if and only if linear discriminant functions 91 , 92 , exist such that . ... and X , is a linear dichotomy if and only if a linear discriminant function g exists such that g ( x ) > 0 g ( x ) < 0 for ...
... X2 , Xr are linearly separable if and only if linear discriminant functions 91 , 92 , exist such that . ... and X , is a linear dichotomy if and only if a linear discriminant function g exists such that g ( x ) > 0 g ( x ) < 0 for ...
Sivu 84
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Sivu 97
There do not yet exist , however , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that Y.W > 0 for each Y in Yi and ( 6.1 ) Y.W < 0 for each Y in Y2 Therefore ...
There do not yet exist , however , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that Y.W > 0 for each Y in Yi and ( 6.1 ) Y.W < 0 for each Y in Y2 Therefore ...
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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |