Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 64
Sivu 16
An important special case of a linear machine is a minimum - distance classifier with respect to points . We shall consider this special case first before discussing the properties of linear machines in general .
An important special case of a linear machine is a minimum - distance classifier with respect to points . We shall consider this special case first before discussing the properties of linear machines in general .
Sivu 20
In the special case in which the linear machine is a minimum - distance classifier , the surface Sij is the hyperplane which is the perpendicular bisector of the line segment joining the points Pi and Pj . Figure 2.3 shows the decision ...
In the special case in which the linear machine is a minimum - distance classifier , the surface Sij is the hyperplane which is the perpendicular bisector of the line segment joining the points Pi and Pj . Figure 2.3 shows the decision ...
Sivu 134
Error - correction training methods , 69 , 80 for committee machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical example of , 71 for linear machines , 75 numerical example of , 72 for $ machines , 76 for ...
Error - correction training methods , 69 , 80 for committee machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical example of , 71 for linear machines , 75 numerical example of , 72 for $ machines , 76 for ...
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 |