- Research Grants
Ricardo J. G. B. Campello received the BSc degree in Electronics Engineering from the State University of São Paulo (Unesp), Ilha Solteira/SP - Brazil, in 1994, and the MSc and Ph.D. degrees in Electrical Engineering from the School of Electrical and Computer Engineering of the State University of Campinas (Unicamp), Campinas/SP - Brazil, in 1997 and 2002, respectively. In 2002 he was a visiting scholar at the Laboratoire D?Informatique, Signaux et Systèmes de Sophia Antipolis, Université de Nice - Sophia Antipolis (UNSA), France. From 2003 through 2006 he worked as an assistant professor in the Graduate Program in Informatics of the Catholic University of Santos, Santos/SP - Brazil, and as an invited researcher of the School of Electrical and Computer Engineering of the State University of Campinas as well. Since 2007 he is with the Department of Computer Sciences of the University of São Paulo (USP) at São Carlos/SP - Brazil, currently as an Associate Professor (from 2011 on). He has also been a merit scholar of the Brazilian National Research Council since 2005. His current research interests fall primarily into the areas of Soft Computing (especially Fuzzy Systems and Evolutionary Computation), Data Mining, Machine Learning, and Modeling of Dynamic Systems. (Source: Lattes Curriculum)
This document formalizes a research proposal for funding from the Research Foundation of the State of São Paulo (FAPESP). The main objective is to support the research group of the applicant for a period of two years. The group is mainly constituted by the researcher and his students at ICMC / USP, but the project will also involve international collaborations with the University of Alb...
Outlier detection plays an important role in the pattern discovery from data that can be considered exceptional in some sense. Detecting such patterns is relevant in general because in many data mining applications, such patterns represent extraordinary behaviors that is worth further analysis. An important distinction is that between the supervised and unsupervised techniques. In this ...
Cluster analysis is a fundamental problem of unsupervised machine learning where the objective is to determine categories that describe a set of objects according to their similarities and inter-relationships. In the traditional formulation of the problem one seeks partitions or hierarchies of partitions containing groups whose objects are in some way similar among themselves and dissim...
There are many situations in machine learning in which it is easy to obtain vast amounts of unlabeled data, but it is costly to obtain labels for these data. This leads to typical situations in which only a small subset of labeled data is available, which is not representative enough for learning a model in a supervised way. In such situations, techniques that are able to learn from bot...
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This document describes a research proposal for a 1 year sabbatical period under the supervision of Prof. Joerg Sander from the Databases (DB) Group of the Department of Computing Science of the University of Alberta (U of A), in Edmonton, Canada. The preliminary conception of this proposal was envisaged during a 1 week visit of Ricardo J. G. B. Campello (candidate) to the DB group of t...
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
|Data from Web of Science|
(References retrieved automatically from State of São Paulo Research Institutions)
JASKOWIAK, Pablo Andretta. Sobre a avaliação de resultados de agrupamento: medidas, comitês e análise de dados de expressão gênica. 2015. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
HORTA, Danilo. Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BATISTA, Antônio José de Lima. Classificação semi-supervisionada ativa baseada em múltiplas hierarquias de agrupamento. 2016. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
HORTA, Danilo. Abordagens evolutivas para agrupamento relacional de dados. 2010. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
NALDI, Murilo Coelho. Técnicas de combinação para agrupamento centralizado e distribuído de dados. 2011. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
PADILHA, Victor Alexandre. Avaliação sistemática de técnicas de bi-agrupamento de dados. 2016. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CAMPELLO, Ricardo José Gabrielli Barreto. Arquiteturas e metodologias para modelagem e controle de sistemas complexos utilizando ferramentas classicas e modernas. 2002. Tese (Doutorado) – Faculdade de Engenharia Eletrica e de Computação. Universidade Estadual de Campinas.