An optimal approach for random signals classification
A method is proposed which solves the problem of the Bayes classification of ARMA (autoregressive moving average) signals when the models of classes and samples are not exactly known but only estimated from finite-length data sequences. Justified approximations and the hypothesis lead to decision ru...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 1991-11, Vol.13 (11), p.1192-1196 |
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| container_end_page | 1196 |
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| container_issue | 11 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 13 |
| creator | Doncarli, C. Le Carpentier, E. |
| description | A method is proposed which solves the problem of the Bayes classification of ARMA (autoregressive moving average) signals when the models of classes and samples are not exactly known but only estimated from finite-length data sequences. Justified approximations and the hypothesis lead to decision rules including the variances of the estimations. The results obtained on a large set of simulated data show that this approach is superior to the best classical methods (cepstral distance or Kullback divergence), particularly in the common case where the hypothesis of those methods is not verified (short samples. small training sets. random classes).< > |
| doi_str_mv | 10.1109/34.103278 |
| format | Article |
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Justified approximations and the hypothesis lead to decision rules including the variances of the estimations. 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| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 1991-11, Vol.13 (11), p.1192-1196 |
| issn | 0162-8828 1939-3539 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28467172 |
| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Graphics Image analysis Image edge detection Image processing Ligaments Pattern analysis Pattern classification Remote sensing Very large scale integration |
| title | An optimal approach for random signals classification |
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