Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 48
Sivu 16
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
Sivu 44
We assume that the p ( xi ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( Xli ) , i = 1 , . . . , R , are normal probability ...
We assume that the p ( xi ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( Xli ) , i = 1 , . . . , R , are normal probability ...
Sivu 49
values are given by ( 1 9 ) Wi = i = ܕܐ . , d . ( 1 pi ) log [ p : I = log 108 [ I PP . ) ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 .
values are given by ( 1 9 ) Wi = i = ܕܐ . , d . ( 1 pi ) log [ p : I = log 108 [ I PP . ) ... Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + 1 .
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank belonging belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance 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 machine linearly separable matrix mean vector 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 solution space Stanford step 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 |
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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |