Online adaptive hierarchical space partitioning classifier
dc.citation.epage | 1240 | en_US |
dc.citation.spage | 1237 | en_US |
dc.contributor.author | Kılıç, O. Fatih | en_US |
dc.contributor.author | Vanlı, N. D. | en_US |
dc.contributor.author | Özkan, Hüseyin | en_US |
dc.contributor.author | Delibalta, İ. | en_US |
dc.contributor.author | Kozat, Süleyman Serdar | en_US |
dc.coverage.spatial | Zonguldak, Turkey | en_US |
dc.date.accessioned | 2018-04-12T11:48:27Z | |
dc.date.available | 2018-04-12T11:48:27Z | |
dc.date.issued | 2016 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 16-19 May 2016 | en_US |
dc.description | Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 | en_US |
dc.description.abstract | We introduce an on-line classification algorithm based on the hierarchical partitioning of the feature space which provides a powerful performance under the defined empirical loss. The algorithm adaptively partitions the feature space and at each region trains a different classifier. As a final classification result algorithm adaptively combines the outputs of these basic models which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets. | en_US |
dc.description.provenance | Made available in DSpace on 2018-04-12T11:48:27Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016 | en |
dc.identifier.doi | 10.1109/SIU.2016.7495970 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37701 | |
dc.language.iso | Turkish | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/SIU.2016.7495970 | en_US |
dc.source.title | Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 | en_US |
dc.subject | Adaptive trees | en_US |
dc.subject | Classification | en_US |
dc.subject | Computational efficiency | en_US |
dc.subject | On-line learning | en_US |
dc.title | Online adaptive hierarchical space partitioning classifier | en_US |
dc.title.alternative | Uyarlanır uzay bölümleme ile çevrimiçi sınıflandırma | en_US |
dc.type | Conference Paper | en_US |
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