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Data classification based on random walk on networks

Grant number: 12/14217-9
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2012
Effective date (End): January 31, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Zhao Liang
Grantee:Filipe Alves Neto Verri
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Through computational representation, the machine learning techniques can generate models capable of to organize the existing knowledge or mimic the behavior of a human expert in the relevant fields. Traditional classification techniques only consider physical features of the data such as distance, similarity and density. These classifiers are called low-level classifiers. Often the observation of these features is not sufficient to classify correctly the data. Other current techniques also consider the features of pattern formation, which have semantic meanings. These techniques are called high-level classifiers. The high-level classification solves many problems such as invariant pattern recognition. In this project, it is proposed to develop a high-level classifier based on networks by using measures related to the random walk. This classifier should be able to look beyond the physical features of the data, the features of the pattern formation to which they belong.