Group approach to solving the tasks of recognition
In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probabi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Belgrade
2019-01-01
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| Series: | Yugoslav Journal of Operations Research |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/0354-0243/2019/0354-02431800032A.pdf |
| Summary: | In this work, we develop CASVM and CANN algorithms for semi-supervised
classification problem. The algorithms are based on a combination of ensemble
clustering and kernel methods. Probabilistic model of classification with use
of cluster ensemble is proposed. Within the model, error probability of CANN
is studied. Assumptions that make probability of error converge to zero are
formulated. The proposed algorithms are experimentally tested on a
hyperspectral image. It is shown that CASVM and CANN are more noise
resistant than standard SVM and kNN. |
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| ISSN: | 0354-0243 1820-743X |