Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 52
origin and falls toward zero away from the origin . Contours of equal probability density ( z12 20122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 eccentricities of the ellipses ...
origin and falls toward zero away from the origin . Contours of equal probability density ( z12 20122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 eccentricities of the ellipses ...
Sivu 67
Note that the point representing the weight values w1 = 0 , w2 ... , WD = 0 satisfies Eq . ( 4.2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . = Corresponding to the training subsets X1 ...
Note that the point representing the weight values w1 = 0 , w2 ... , WD = 0 satisfies Eq . ( 4.2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . = Corresponding to the training subsets X1 ...
Sivu 85
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
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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 |