Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The parametric training method in this case would use the training set to derive estimates of X1 and X2 . Suppose the training set consisted of N1 patterns belonging to category 1 and N2 patterns belonging to cate- gory 2.
The parametric training method in this case would use the training set to derive estimates of X1 and X2 . Suppose the training set consisted of N1 patterns belonging to category 1 and N2 patterns belonging to cate- gory 2.
Sivu 49
We then presume that these estimates are the true values and use them in Eq . ( 3.14 ) to specify the discriminant ... Thus , N1 N2 = N. Reasonable * estimates for the unknown probabili- + * The reader with background in statistics will ...
We then presume that these estimates are the true values and use them in Eq . ( 3.14 ) to specify the discriminant ... Thus , N1 N2 = N. Reasonable * estimates for the unknown probabili- + * The reader with background in statistics will ...
Sivu 58
The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler ...
The ( X ) ; and ( ) ; are reasonable * estimates of M ; and Σ , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler ...
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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 |