Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 60
Sivu 69
4.3 TLU training procedures Suppose that we have a linear dichotomy of y with two subsets Y1 and Y2 and that for some pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ...
4.3 TLU training procedures Suppose that we have a linear dichotomy of y with two subsets Y1 and Y2 and that for some pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ...
Sivu 75
4.5 An error - correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than ... Each discriminant function can be represented as the dot product of a weight vector with an augmented pattern ...
4.5 An error - correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than ... Each discriminant function can be represented as the dot product of a weight vector with an augmented pattern ...
Sivu 103
are adjusted as shown since they are the closest to the Y1 pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
are adjusted as shown since they are the closest to the Y1 pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
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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 |