Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Figure 2-3 shows the decision regions and surfaces for the minimum - distance classifier with respect to the two ... 2-2 and 2-3 that the decision regions are convex ( a region is convex if and only if the straight - line segment con- ...
Figure 2-3 shows the decision regions and surfaces for the minimum - distance classifier with respect to the two ... 2-2 and 2-3 that the decision regions are convex ( a region is convex if and only if the straight - line segment con- ...
Sivu 67
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern ...
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern ...
Sivu 68
sponding region in weight space is called the solution region . It is a con- vex region containing all of the solution weight points W satisfying inequality ( 4.3 ) . These ideas are illustrated in Fig . 4.1 for a two - dimensional ...
sponding region in weight space is called the solution region . It is a con- vex region containing all of the solution weight points W satisfying inequality ( 4.3 ) . These ideas are illustrated in Fig . 4.1 for a two - dimensional ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |