Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 16
Sivu 6
Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS.
Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS.
Sivu 106
Note that in this example the two subsets I , ( 1 ) and 12 ( 1 ) of imagespace vertices are linearly separable ; the plane shown is one which separates these subsets . Thus a two - layer machine with three TLUs in the first layer is ...
Note that in this example the two subsets I , ( 1 ) and 12 ( 1 ) of imagespace vertices are linearly separable ; the plane shown is one which separates these subsets . Thus a two - layer machine with three TLUs in the first layer is ...
Sivu 107
Thus , the plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine ...
Thus , the plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine ...
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 |