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Multi-class support vector machine active learning for music annotation

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dc.contributor.author Chen, Gang
dc.contributor.author Wang, Tian-Jiang
dc.contributor.author Gong, Li-Yu
dc.contributor.author Herrera Boyer, Perfecto, 1964-
dc.date.accessioned 2019-05-29T15:24:12Z
dc.date.available 2019-05-29T15:24:12Z
dc.date.issued 2010
dc.identifier.citation Chen G, Wang TJ, Gong L, Herrera P. Multi-class support vector machine active learning for music annotation. International journal of innovative computing and applications. 2010;6(3):921-30.
dc.identifier.issn 1751-648X
dc.identifier.uri http://hdl.handle.net/10230/41653
dc.description.abstract Music annotation is an important research topic in the multimedia area. One of the challenges in music annotation is how to reduce the human effort in labeling music files for building reliable classification models. In the past, there have been many studies on applying support vector machine active learning methods to automatic multimedia data annotation, which try to select the most informative examples for labeling manually. Most of these studies focused on selecting a single unlabeled example in each iteration process for binary classification. As a result, the model has to be retrained after each labeled example is solicited, and the user is likely to lose patience after a few rounds of labeling. In this paper, we present a novel multi-class active learning algorithm that can select multiple music examples for labeling in each iteration process. The key of the multi-sample selection for multi-class active learning is how to reduce the redundancy and avoid selecting the outliers among the selected examples such that each example provides unique information for model updating. To this end, we propose the distance diversity and set density in the support vector machine feature space as the measurement of the scatter of the selected sample set. Experiment results on two music data sets demonstrate the effectiveness of our method. Moreover, although our criterion is designed for music annotation, it can be used in a general frame work.
dc.description.sponsorship This work is partially supported by a grant from 863 Program of China (No.2007AA01Z161).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Inderscience Enterprises Ltd
dc.relation.ispartof International journal of innovative computing and applications. 2010;6(3):921-30.
dc.rights Llicència Creative Commons Attribution-Non-commercial-No Derivatives Licence (www.creativecommons.org/licenses/by-nc-nd/3.0/)
dc.rights.uri http://www.creativecommons.org/licenses/by-nc-nd/3.0/
dc.title Multi-class support vector machine active learning for music annotation
dc.type info:eu-repo/semantics/article
dc.subject.keyword Support vector machine
dc.subject.keyword Active learning
dc.subject.keyword Relevance feedback
dc.subject.keyword Music annotation
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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