O dumping e as normas internas de proteção à concorrência empresarial

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A eficiência e a eficácia das normas antidumping na OMC e suas repercussões no direito concorrencial brasileiro

Pós-graduação em Direito - FCHS

O processo de desenvolvimento geoeconômico do complexo agroindustrial cooperativista na mesorregião norte central paranaense

Pós-graduação em Geografia - FCT

Ação de biorreguladores na brotação, produção e algumas características físico-químicas de uva do cultivar Superior Seedless

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Os sentidos da ciência no cenário industrial: a inovação tecnológica e suas implicações na política de educação superior

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Inovação, competição internacional e transição hegemônica: a política científico-tecnológica dos Estados Unidos para evitar o declínio no século XXI

Pós-graduação em Relações Internacionais (UNESP - UNICAMP - PUC-SP) - FFC

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.