Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 18
Sivu 36
... d ) of linear dichotomies of X achievable by a hyperplane constrained to contain all the points of Z. We shall ... Z are in general position , meaning , in this case , that no ( K – 2 ) -dimensional hyperplane contains all of them .
... d ) of linear dichotomies of X achievable by a hyperplane constrained to contain all the points of Z. We shall ... Z are in general position , meaning , in this case , that no ( K – 2 ) -dimensional hyperplane contains all of them .
Sivu 102
( The reader could assume , for example , that Yi contains Y , and that Y2 contains – Y , and – Y3 . ) ... The shaded regions indicate those regions that must each contain one of the weight vectors before the process can successfully ...
( The reader could assume , for example , that Yi contains Y , and that Y2 contains – Y , and – Y3 . ) ... The shaded regions indicate those regions that must each contain one of the weight vectors before the process can successfully ...
Sivu 105
In this example there are four cells that contain pattern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to a ...
In this example there are four cells that contain pattern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to 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 |
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 |