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Doctoral Thesis
DOI
10.11606/T.55.2010.tde-21092010-104722
Document
Author
Full name
Fabricio Aparecido Breve
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2010
Supervisor
Committee
Liang, Zhao (President)
Piqueira, Jose Roberto Castilho
Romero, Roseli Aparecida Francelin
Soma, Nei Yoshihiro
Tinós, Renato
Title in Portuguese
Aprendizado de máquina em redes complexas
Keywords in Portuguese
Aprendizado de máquina
Dinâmica espaço-temporal
Redes complexas
Abstract in Portuguese
Redes complexas é um campo de pesquisa científica recente e bastante ativo que estuda redes de larga escala com estruturas topológicas não triviais, tais como redes de computadores, redes de telecomunicações, redes de transporte, redes sociais e redes biológicas. Muitas destas redes são naturalmente divididas em comunidades ou módulos e, portanto, descobrir a estrutura dessas comunidades é um dos principais problemas abordados no estudo de redes complexas. Tal problema está relacionado com o campo de aprendizado de máquina, que tem como interesse projetar e desenvolver algoritmos e técnicas que permitem aos computadores aprender, ou melhorar seu desempenho através da experiência. Alguns dos problemas identificados nas técnicas tradicionais de aprendizado incluem: dificuldades em identificar formas irregulares no espaço de atributos; descobrir estruturas sobrepostas de grupos ou classes, que ocorre quando elementos pertencem a mais de um grupo ou classe; e a alta complexidade computacional de alguns modelos, que impedem sua aplicação em bases de dados maiores. Neste trabalho tratamos tais problemas através do desenvolvimento de novos modelos de aprendizado de máquina utilizando redes complexas e dinâmica espaço-temporal, com capacidade para tratar grupos e classes sobrepostas, além de fornecer graus de pertinência para cada elemento da rede com relação a cada cluster ou classe. Os modelos desenvolvidos tem desempenho similar ao de algoritmos do estado da arte, ao mesmo tempo em que apresentam ordem de complexidade computacional menor do que a maioria deles
Title in English
Machine learning in complex networks
Keywords in English
Complex networks
Machine learning
Space-temporal dynamics
Abstract in English
Complex networks is a recent and active scientific research field, which concerns large scale networks with non-trivial topological structure, such as computer networks, telecommunication networks, transport networks, social networks and biological networks. Many of these networks are naturally divided into communities or modules and, therefore, uncovering their structure is one of the main problems related to complex networks study. This problem is related with the machine learning field, which is concerned with the design and development of algorithms and techniques which allow computers to learn, or increase their performance based on experience. Some of the problems identified in traditional learning techniques include: difficulties in identifying irregular forms in the attributes space; uncovering overlap structures of groups or classes, which occurs when elements belong to more than one group or class; and the high computational complexity of some models, which prevents their application in larger data bases. In this work, we deal with these problems through the development of new machine learning models using complex networks and space-temporal dynamics. The developed models have performance similar to those from some state-of-the-art algorithms, at the same time that they present lower computational complexity order than most of them
 
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Publishing Date
2010-09-21
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • BREVE, Fabricio A., et al. Chaotic phase synchronization and desynchronization in an oscillator network for object selection [doi:10.1016/j.neunet.2009.06.027]. Neural Networks [online], 2009, vol. 22, n. -1, p. 728-737.
  • BREVE, Fabricio, et al. Particle Competition and Cooperation in Networks for Semi-Supervised Learning [doi:10.1109/TKDE.2011.119]. IEEE Transactions on Knowledge and Data Engineering [online], 2011.
  • QUILES, Marcos G., et al. A network of integrate and fire neurons for visual selection [doi:10.1016/j.neucom.2008.10.024]. Neurocomputing [online], 2009, vol. 72, n. -2, p. 2198-2208.
  • ZHAO, Liang, and BREVE, Fabricio Aparecido. Chaotic synchronization in 2D lattice for scene segmentation [doi:10.1016/j.neucom.2007.09.011]. Neurocomputing [online], 2008, vol. 71, n. -2, p. 2761-2771.
  • BREVE, Fabricio A., et al. Chaotic phase synchronization for visual selection [doi:10.1109/IJCNN.2009.5178761]. In 2009 International Joint Conference on Neural Networks [online], Atlanta, Ga, USA, 2009. Atlanta, Ga, USA : IEEE, 2009. p. 383-390. ISBN 978-1-4244-3548-7.
  • BREVE, Fabricio A., ZHAO, Liang, and QUILES, Marcos G.. Semi-supervised learning from imperfect data through particle cooperation and competition [doi:10.1109/IJCNN.2010.5596659]. In The 2010 International Joint Conference on Neural Networks (IJCNN) [online], Barcelona, Spain, 2010. Barcelona, Spain : IEEE, 2010. p. 1-8. ISBN 978-1-4244-6916-1.
  • BREVE, Fabricio, and ZHAO, Liang. Particle competition and cooperation in networks for semi-supervised learning with concept drift [doi:10.1109/IJCNN.2012.6252617]. In The 2012 International Joint Conference on Neural Networks (IJCNN) [online], Brisbane, Australia, 2012. Brisbane, Australia : IEEE, 2012. p. 1-6. ISBN 978-1-4673-1489-3.
  • QUILES, Marcos G., et al. Label propagation through neuronal synchrony [doi:10.1109/IJCNN.2010.5596809]. In The 2010 International Joint Conference on Neural Networks (IJCNN) [online], Barcelona, Spain, 2010. Barcelona, Spain : IEEE, 2010. p. 1-8. ISBN 978-1-4244-6916-1.
  • QUILES, Marcos G., et al. Visual Selection with Feature Contrast-Based Inhibition in a Network of Integrate and Fire Neurons [doi:10.1109/icnc.2008.209]. In 2008 Fourth International Conference on Natural Computation [online], Jinan, Shandong, China, 2008. Jinan, Shandong, China : IEEE, 2008. p. 601-605. ISBN 978-0-7695-3304-9.
  • QUILES, Marcos, et al. A Visual Selection Mechanism Based on Network of Chaotic Wilson-Cowan Oscillators [doi:10.1109/ISDA.2007.4389725]. In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) [online], Rio de Janeiro, Brazil, 2007. Rio de Janeiro, Brazil : IEEE, 2007. p. 919-924. ISBN 978-0-7695-2976-9.
  • ZHAO, Liang, et al. Visual Selection and Shifting Mechanisms Based on a Network of Chaotic Wilson-Cowan Oscillators [doi:10.1109/icnc.2007.811]. In Third International Conference on Natural Computation (ICNC 2007) Vol V [online], Haikou, China, 2007. Haikou, China : IEEE, 2007. p. 754-762. ISBN 978-0-7695-2875-5.
  • DENG, Hepu, et al. Artificial Intelligence and Computational Intelligence [doi:10.1007/978-3-642-05253-8_68]. Editor. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. chap. 68, Uncovering Overlap Community Structure in Complex Networks Using Particle Competition, p. 619-628. Lecture Notes in Computer Science.
  • LIU, Derong, et al. Advances in Neural Networks – ISNN 2011 [doi:10.1007/978-3-642-21111-9_48]. Editor. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. chap. 48, Particle Competition and Cooperation for Uncovering Network Overlap Community Structure, p. 426-433. Lecture Notes in Computer Science.
  • ZHOU, Jie. Complex Sciences [doi:10.1007/978-3-642-02466-5_14]. Editor. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. chap. 14, Particle Competition in Complex Networks for Semi-supervised Classification, p. 163-174. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engi.
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