Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 24
... case of discriminant functions which we shall call piecewise linear , we shall first consider those of a minimum - distance classifier with respect to point sets . i = Suppose we are given R finite point sets P1 , 9 P2 , PR .
... case of discriminant functions which we shall call piecewise linear , we shall first consider those of a minimum - distance classifier with respect to point sets . i = Suppose we are given R finite point sets P1 , 9 P2 , PR .
Sivu 26
Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum - distance classi- fier with respect to point sets is a special case . P1 P 1 2 = = R2 R1 ހ { P , P2 } 1 ( 1 ) ...
Any machine employing piecewise linear discriminant functions will be called a piecewise linear machine , of which a minimum - distance classi- fier with respect to point sets is a special case . P1 P 1 2 = = R2 R1 ހ { P , P2 } 1 ( 1 ) ...
Sivu 135
... 77 Mean vector of normal patterns , 53 , 54 Measurements , 4 selection of , 4 Miller , 12 , 13 Minimization of Euclidean distances , 132 Minimum - distance classifiers , with respect to point sets , 24 , 121 with respect to points ...
... 77 Mean vector of normal patterns , 53 , 54 Measurements , 4 selection of , 4 Miller , 12 , 13 Minimization of Euclidean distances , 132 Minimum - distance classifiers , with respect to point sets , 24 , 121 with respect to points ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ 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 |