@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",
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}, AL, Brazil",
conference-year = "28-31 Aug. 2011",
doi = "10.1109/SIBGRAPI.2011.19",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2011.19",
language = "en",
ibi = "8JMKD3MGPBW34M/3A3LJ5H",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3A3LJ5H",
targetfile = "SIBGRAPI2011_Classification.pdf",
urlaccessdate = "2024, Apr. 29"
}