Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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... 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 65
The subset X1 contains those patterns belonging to category 1 , and X2 contains those patterns belonging to category 2. In this chapter we shall assume that the training subsets are linearly separable . Based only on this assumption we ...
The subset X1 contains those patterns belonging to category 1 , and X2 contains those patterns belonging to category 2. In this chapter we shall assume that the training subsets are linearly separable . Based only on this assumption we ...
Sivu 102
( The reader could assume , for example , that Y1 contains Y2 and that y2 contains - Y1 and —Y .. ) ... 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 —Y .. ) ... The shaded regions indicate those regions that must each contain one of the weight vectors before the proc- ess can successfully ...
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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 |