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@InProceedings{SilvaCupeZhao:2011:HiLeCl,
               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 = "High Level Classification for Pattern Recognition",
            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 = "high level classification, complex networks.",
             abstract = "Traditional data classification techniques consider only physical 
                         features of input data in order to construct their hypotheses. On 
                         the other hand, the human (animal) brain performs both low and 
                         high order learning and it has facility to identify patterns 
                         according to the semantic meaning of input data. In this paper, we 
                         propose a data classification technique by combining the low level 
                         and the high level learning. The low level term can be implemented 
                         by any classification technique, while the high level 
                         classification is realized by the extraction of features of the 
                         underlying network constructed from the input data. Thus, the 
                         former classifies data instances by their physical features, while 
                         the latter measures the compliance to the pattern formation of the 
                         data. Our study shows that the proposed technique can not only 
                         realize classification according to the pattern formation, but it 
                         is also able to improve the performance of traditional 
                         classification techniques. An application on handwritten digits 
                         recognition is performed, revealing that higher classification 
                         rates can be obtained when we have a proper mixture of low and 
                         high level classifiers.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "SIBGRAPI2011_Classification.pdf",
        urlaccessdate = "2019, Dec. 09"
}


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