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@InProceedings{SilvaCupeZhao:2011:StCoLe,
               author = "Silva, Thiago Christiano and Cupertino, Thiago Henrique and Zhao, 
                         Liang",
          affiliation = "Department of Computer Sciences, Institute of Mathematics and 
                         Computer Science (ICMC), University of S{\~a}o Paulo (USP) and 
                         Department of Computer Sciences, Institute of Mathematics and 
                         Computer Science (ICMC), University of S{\~a}o Paulo (USP) and 
                         Department of Computer Sciences, Institute of Mathematics and 
                         Computer Science (ICMC), University of S{\~a}o Paulo (USP)",
                title = "Stochastic Competitive Learning Applied to Handwritten Digit and 
                         Letter Clustering",
            booktitle = "Proceedings...",
                 year = "2011",
               editor = "Lewiner, Thomas and Torres, Ricardo",
         organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
            publisher = "IEEE Computer Society Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "stochastic competitive learning, handwritten pattern clustering.",
             abstract = "Competitive learning is an important mechanism for data clustering 
                         and pattern recognition. In this paper, we present a rigorous 
                         definition of a new type of competitive learning scheme realized 
                         on large scale networks. In this model, several particles walk in 
                         the network and compete with each other to occupy as many nodes as 
                         possible, while attempting to reject intruder particles. As a 
                         result, each particle will dominate a cluster of the network. 
                         Moreover, we propose an efficient method for determining the right 
                         number of clusters by using the information generated by the 
                         competition process itself, avoiding the calculation of an 
                         external evaluating index. In this work, we apply the model to 
                         handwritten data clustering. Computer simulations reveal that the 
                         proposed technique obtains satisfactory cluster detection 
                         accuracy.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "SIBGRAPI2011_ParticleCompetition.pdf",
        urlaccessdate = "2019, Dec. 09"
}


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