Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Suppose the training set consisted of N1 patterns belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in ...
Suppose the training set consisted of N1 patterns belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in ...
Sivu 57
Suppose that a training set of typical patterns belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , . . . , XR , where X is the training subset of all patterns belonging to category i .
Suppose that a training set of typical patterns belonging to each of the R categories is available . It consists of R subsets denoted by X1 , X2 , . . . , XR , where X is the training subset of all patterns belonging to category i .
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4.5 An error - correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
4.5 An error - correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
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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 |