Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then ...
Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then ...
Sivu 92
5.7 Bibliographical and historical remarks The first proof of Theorem 5.1 was outlined by Rosenblatt . Subsequent proofs have been given by Joseph , 2 Block ...
5.7 Bibliographical and historical remarks The first proof of Theorem 5.1 was outlined by Rosenblatt . Subsequent proofs have been given by Joseph , 2 Block ...
Sivu 134
... 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training ...
... 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable measurements negative networks 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 Stanford step Suppose theorem theory threshold training methods 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |