Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
Sivu 40
TABLE 2.3 The capacities of some Ž machines Decision boundary in pattern space implemented by machine Capacity Hyperplane 2 ( d + 1 ) Hypersphere General quadric surface rth - order polynomial surface 2 ( d + 2 ) ( d + 1 ) ( d + 2 ) 2 ...
TABLE 2.3 The capacities of some Ž machines Decision boundary in pattern space implemented by machine Capacity Hyperplane 2 ( d + 1 ) Hypersphere General quadric surface rth - order polynomial surface 2 ( d + 2 ) ( d + 1 ) ( d + 2 ) 2 ...
Sivu 76
From Chapter 2 we recall that a machine can be implemented by a processor followed by a linear machine . The processor converts the set X of d - dimensional pattern vectors into a set F of M - dimensional vectors by the mapping F = F ...
From Chapter 2 we recall that a machine can be implemented by a processor followed by a linear machine . The processor converts the set X of d - dimensional pattern vectors into a set F of M - dimensional vectors by the mapping F = F ...
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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,Lįszló 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 |