Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 49
We then presume that these estimates are the true values and use them in Eq . ( 3.14 ) to specify the discriminant function . ... Thus , N1 + N2 = N. Reasonable * estimates for the unknown probabili- 1 * The reader with background in ...
We then presume that these estimates are the true values and use them in Eq . ( 3.14 ) to specify the discriminant function . ... Thus , N1 + N2 = N. Reasonable * estimates for the unknown probabili- 1 * The reader with background in ...
Sivu 58
The ( X ) ; and ( Σ ) ; are reasonable * estimates of M ; and ; , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . An expression that is somewhat simpler ...
The ( X ) ; and ( Σ ) ; are reasonable * estimates of M ; and ; , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . An expression that is somewhat simpler ...
Sivu 120
The Fix and Hodges procedure clearly is an attempt to estimate the values of p ( xi ) p ( i ) for i = 1 , R around ... variations of the probability distributions with X. In any case , if the training subsets are small , the estimates ...
The Fix and Hodges procedure clearly is an attempt to estimate the values of p ( xi ) p ( i ) for i = 1 , R around ... variations of the probability distributions with X. In any case , if the training subsets are small , the estimates ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |