LUIS TORGO PDF

Proceedings of KDNet Symposium on Knowledge-based systems for the Public Sector, , Functional models for regression tree leaves. L Torgo. List of computer science publications by Luís Torgo. Luis Torgo is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto, Portugal. He is a senior.

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In that case, your data will be automatically deleted from our information system. RibeiroBernhard Pfahringer: PMLR88, pp.

We created sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. Outlier detection using clustering methods: Luid of Discovery Science Predicting Rare Extreme Values. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. Their combined citations are counted only for the first article. Applications to Financial Trading.

My research tprgo around the general area of Data Science, with a strong focus luiss Predictive Analytics. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Regression by classification L Torgo, J Gama Brazilian symposium on artificial intelligence, Learning with Imbalanced Domains: Most of the main data mining processes and techniques are covered in the book by means of the presentation of four detailed case studies: He has been involved in many research projects under different roles and involving different types of organizations.

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Luis Torgo

Arbitrage of Forecasting Experts With an extensive set of experiments, we provide evidence of the advantage of introducing a neighbourhood bias in the resampling strategies for both classification and regression tasks with imbalanced data sets. Dynamic Discretization of Continuous Attributes.

By continuing to browse our site you accept our cookie policy. Resampling with Neighbourhood Bias on Imbalanced Domains. Portuguese conference on artificial intelligence, Interval Forecast of Water Quality Parameters.

Staying Zombies or Awaiting for Resurrection? Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples.

Potential of dissimilatory nitrate reduction pathways in polycyclic aromatic hydrocarbon degradation. Data Mining with R. Rita Ribeiro Utility-based Regression. Paula BrancoRita P.

Luís Torgo – Google Scholar Citations

Meta Learning for Utility Maximization in Regression. Learning with Case Studies. A strongly revised and extended Second Edition is out – check it! Articles Cited by Co-authors.

Data Mining with R: In this paper we focus on sentiment classification of Twitter data. Title Cited by Year Data mining with R: In this work, we propose variants of existing resampling strategies that are able to take into account the information regarding the neighbourhood of the examples.

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