Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 60
Sivu 69
Suppose that we have a linear dichotomy of Y with two subsets Yi and Y2 a 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 · W = 0 ) .
Suppose that we have a linear dichotomy of Y with two subsets Yi and Y2 a 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 · W = 0 ) .
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 102
those that are closest to this pattern hyperplane are adjusted by the addition of the pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6.5 .
those that are closest to this pattern hyperplane are adjusted by the addition of the pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6.5 .
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |