In this paper we propose an approach for incremental learning of semi-supervised SVM. The proposed approach makes use of the locality of radial basis function kernels to do local and incremental training of semi-supervised support vector machines. The algorithm introduces a se- quential minimal optimization based implementation of the branch and bound technique for training semi-supervised SVM problems. The novelty of our approach lies in the in the introduction of incremental learning techniques to semisupervised SVMs.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:78844 |
| Date | 11 May 2022 |
| Creators | Emara, Wael, Karnstedt, Mehmed Kantardzic Marcel, Sattler, Kai-Uwe, Habich, Dirk, Lehner, Wolfgang |
| Publisher | IEEE |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English, German |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-0-7695-3019-2, 2375-9259, 10.1109/ICDMW.2007.106 |
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