Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 87
5.5 A training theorem for R - category linear machines , ; . > = > Suppose we are given R finite subsets of augmented training pattern vectors Yı , Y2 , Yr . These subsets are linearly separable if and only if there exist R solution ...
5.5 A training theorem for R - category linear machines , ; . > = > Suppose we are given R finite subsets of augmented training pattern vectors Yı , Y2 , Yr . These subsets are linearly separable if and only if there exist R solution ...
Sivu 119
It should be observed that if the two density functions overlap sufficiently , it is likely that this optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume ...
It should be observed that if the two density functions overlap sufficiently , it is likely that this optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume ...
Sivu 120
function that depends on the geometric arrangement of the patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the ...
function that depends on the geometric arrangement of the patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the ...
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adjusted apply assume bank belonging belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance 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 implemented important initial layered machine linear dichotomies linear machine linearly separable matrix mean vector 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 |