Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 36
... d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall ... are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them .
... d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall ... are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them .
Sivu 102
( The reader could assume , for example , that Y1 contains Y2 and that Y2 contains - Y1 and -Y3 . ) ... The shaded regions indicate those regions that must each contain one of the weight vectors before the proc- ess can successfully ...
( The reader could assume , for example , that Y1 contains Y2 and that Y2 contains - Y1 and -Y3 . ) ... The shaded regions indicate those regions that must each contain one of the weight vectors before the proc- ess can successfully ...
Sivu 105
In this example there are four cells that contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to ...
In this example there are four cells that contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |