Polidocanol versus hypertonic glucose for sclerotherapy treatment of reticular veins of the lower limbs: study protocol for a randomized controlled trial

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

Efeitos da ciclosporina na lesão renal por isquemia-reperfusão em ratos: estudo em modelo experimental

Pós-graduação em Anestesiologia - FMB

Ferramenta computacional para identificação de micro-organismos com base em assinaturas genômicas

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

REML/BLUP para predição de valores genotípicos de topcrosses e seleção de testadores em milho

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Entendendo o comportamento canino: estudo das causas de agressão e sua influência na profilaxia da raiva humana

Pós-graduação em Medicina Veterinária - FCAV

Métodos de classificação de imagens na identificação de áreas cultivadas com citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparação de métodos de classificação de imagens na identificação de áreas cultivadas com citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Avaliação da função tireoidiana, iodúria e estresse oxidativo em gestantes

Pós-graduação em Alimentos e Nutrição - FCFAR

Mapeamento geológico-geotécnico por meio de análise integrada no Parque Estadual do Juquery - Franco da Rocha-SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

Usinagem do alumínio da classe 6005 com uso de ferramenta cerâmica

The machining process is so much important in the economic world. Many machining parameters have been studied to maximize results, in terms of cost and lifetime. (decrease of cutting tool wear, improved surface finish, among others). The objective of this study is to evaluate the wear of a ceramic tool in the machining of the aluminum alloy 6005 A. The analysis of the wear of the cutting tools is very important due to its big impact on the final finishing of the piece as a whole. The evaluation took place in two stages, first it was done a detailed study of the literature of the whole machining process, where the study of the formation and swarf classification were among the most important steps in this phase. The second step consisted in the machining of the piece of aluminum 6005 A with a ceramic cutting tool constituded of aluminum oxide and magnesium oxide with silicon carbide impregnation. The swarf generated in this process was then photographed with a Zeiss optical microscope and analyzed for its size and shape. Through this comparison it was concluded that the swarf are generated shear swarfs, shaped like a tangled, fragmented and arcs connected, thus classifying the material as medium difficulty machining. Through the image analysis tool it was concluded that the parameter of lower wear was the: Vc = 500m / min, f = 0.10mm / rev and ap = 0.5mm

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efficient supervised optimum-path forest classification for large datasets

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

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.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.