Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Because the decision regions of a linear machine are convex , it is easy XR are linearly separable , then to show that if the subsets X1 , X2 , each pair of subsets Xi , X ,, i , j = 1 , ... 9 R , ij , is also linearly sepa- rable .
Because the decision regions of a linear machine are convex , it is easy XR are linearly separable , then to show that if the subsets X1 , X2 , each pair of subsets Xi , X ,, i , j = 1 , ... 9 R , ij , is also linearly sepa- rable .
Sivu 87
These subsets are linearly separable if and only if there exist R solution weight vectors W1 , W2 , • · · " WR such that " R , i #j ( 5 · 28 ) Y.W , Y. W ; for each Ye Yi i , j = 1 , i If the subsets are linearly separable , then a ...
These subsets are linearly separable if and only if there exist R solution weight vectors W1 , W2 , • · · " WR such that " R , i #j ( 5 · 28 ) Y.W , Y. W ; for each Ye Yi i , j = 1 , i If the subsets are linearly separable , then a ...
Sivu 107
When these conditions are met , ( 1 ) and ( 1 ) are guaranteed to be linearly separable , and thus a two - layer machine ... 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient ...
When these conditions are met , ( 1 ) and ( 1 ) are guaranteed to be linearly separable , and thus a two - layer machine ... 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient ...
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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 |