@ARTICLE{kne_ismir04,
    Author         = {P. Knees and E. Pampalk and G. Widmer},
    Month          = {October 10-14}
    year = 2004,
    title = {Artist Classifiction with Web-Based Data},
    booktitle = {Proceedings of the Fifth International Conference on Music
                 Information Retrieval {(ISMIR'04)},
    address = {Barcelona, Spain},
    abstract = {Manifold approaches exist for organization of music by 
      genre and/or style. In this paper we propose the use of text categorization
      techniques to classify artists present on the Internet. In particular, 
      we retrieve and analyze webpages ranked by search engines to describe 
      artists in terms of word occurrences on related pages. To classify artists 
      we primarily use support vector machines.

      We present 3 experiments in which we address the following issues. First, 
      we study the performance of our approach compared to previous work. Second, 
      we investigate how daily fluctuations in the Internet affect our approach. 
      Third, on a set of 224 artists from 14 genres we study (a) how many artists 
      are necessary to define the concept of a genre, (b) which search engines 
      perform best, (c) how to formulate search queries best, (d) which overall 
      performance we can expect for classification, and finally (e) how our approach 
      is suited as a similarity measure for artists.},
    publisher = {Universitat Pompeu Fabra}
}