Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 60
Sivu 56
E ' + 2 ' M ( 2 ) - 1 2 ( 3.34 ) = The hyperplane decision boundary given by g ( x ) = 0 is normal to the line segment connecting the transformed means 2 - M , and 2 - M2 . Its * For example , the covariance matrices describing the ...
E ' + 2 ' M ( 2 ) - 1 2 ( 3.34 ) = The hyperplane decision boundary given by g ( x ) = 0 is normal to the line segment connecting the transformed means 2 - M , and 2 - M2 . Its * For example , the covariance matrices describing the ...
Sivu 104
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUs , we can say that it transforms the vertices of ...
For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second layer of TLUs , we can say that it transforms the vertices of ...
Sivu 121
The nonparametric decision rule to be described assumes the existence of a method to find good estimates for these modes , given the training subsets . Suppose the modes for the various categories , as established by a training ...
The nonparametric decision rule to be described assumes the existence of a method to find good estimates for these modes , given the training subsets . Suppose the modes for the various categories , as established by a training ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
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 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 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 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |