Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward ...
3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward ...
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3.7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
3.7 The multivariate normal distribution The matrix notation of the last section has a direct parallel in the case in which the number of pattern components is greater than two . There is a multivariate normal distribution which ...
Sivu 55
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for normal patterns ...
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for normal patterns ...
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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 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 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 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |