Music Retrieval based on a Multi-Samples Selection Strategy for Support Vector Machine Active Learning

TitleMusic Retrieval based on a Multi-Samples Selection Strategy for Support Vector Machine Active Learning
Publication TypeConference Paper
Year of Publication2009
Conference Name24th Annual ACM Symposium on Applied Computing
AuthorsWang, T. - G., Chen G., & Herrera P.
Conference Start Date09/03/2009
Conference LocationHonolulu, Hawaii, USA
AbstractIn active learning based music retrieval systems, providing multiple samples to the user for feedback is very necessary. In this paper, we present a new multi-samples selection strategy designed for support vector machine active learning. Aiming to reduce the redundancy between the selected samples, the strategy enforces the selected samples to be diverse by explicitly maximizing the distance between each other in the feature space. Experimental results on a music genre database demonstrated the effectiveness of the proposed strategy in selecting relevant multiple samples for human feedback on them.
preprint/postprint documenthttp://mtg.upf.edu/files/publications/Wang-ACM-2008.pdf
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