Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 32
... dimensional face . Even when the pattern points are not in general position ,
the derivation to follow yields a useful upper bound on L ( N , d ) . * In some of the
derivations to follow in this 32 SOME IMPORTANT DISCRIMINANT FUNCTIONS.
... dimensional face . Even when the pattern points are not in general position ,
the derivation to follow yields a useful upper bound on L ( N , d ) . * In some of the
derivations to follow in this 32 SOME IMPORTANT DISCRIMINANT FUNCTIONS.
Sivu 62
5 , in which the patterns were composed of independent , random , binary
components , follows an unpublished derivation given by J . W . Jones of
International Telephone and Telegraph . A similar derivation is given by Minsky . :
Winder4 has ...
5 , in which the patterns were composed of independent , random , binary
components , follows an unpublished derivation given by J . W . Jones of
International Telephone and Telegraph . A similar derivation is given by Minsky . :
Winder4 has ...
Sivu 77
11 The alternative derivation of L ( N , d ) given in the footnote on page 67 follows
the derivation by Cameron . 12 The error - correction training procedures
discussed in Sec . 4 : 3 stem from a variety of sources . The fixed - increment and
...
11 The alternative derivation of L ( N , d ) given in the footnote on page 67 follows
the derivation by Cameron . 12 The error - correction training procedures
discussed in Sec . 4 : 3 stem from a variety of sources . The fixed - increment and
...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 specific 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |