Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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We see then that the effect of the K constraints imposed by Z is to reduce the dimensionality of the space by K. We then have Lz ( N , d ) = L ( N , d - K ) ( 2.36 ) 2.15 The number of function dichotomies Suppose our discriminant ...
We see then that the effect of the K constraints imposed by Z is to reduce the dimensionality of the space by K. We then have Lz ( N , d ) = L ( N , d - K ) ( 2.36 ) 2.15 The number of function dichotomies Suppose our discriminant ...
Sivu 66
We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by ...
We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by ...
Sivu 67
locus of all weight points for which W.Y = 0 ( 4.2 ) The hyperplane in weight space defined by Eq . ( 4-2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two ...
locus of all weight points for which W.Y = 0 ( 4.2 ) The hyperplane in weight space defined by Eq . ( 4-2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two ...
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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 |