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LUIS TORGO PDF

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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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R academic applied-research basic-research biology concluded consulting-projects cost-sensitive learning costs ensembles evaluation feature engineering imbalance distributions imbalanced distributions imbalanced domains metal learning ongoing ongoing-projects past-projects phd postdoc regression trees relational learning spatiotemporal text mining time series utility utility’based learning.

His current broad research interests revolve around analyzing data from ulis environments, with a particular focus on time and space-time dependent data sets, in the search for unexpected events. Predicting Harmful Algae Blooms. A comparative study of approaches to forecast the correct trading actions.

He has been involved in many research projects under different roles and involving different types of organizations. If you choose to, you can easily unsubscribe from the newsletter by following the link presented in the footer.

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Paula Branco Utility-based Predictive Analytics.

Luis Torgo main contributions to the state of the art on data mining and machine learning are related with tree-based regression methods and more recently with utility-based forecasting methods. Aquatic Microbial Ecology80 2pp.

Luís Torgo

The following articles are merged in Scholar. Error Estimators for Pruning Regression Trees.

This book is about learning kuis to use R for performing data mining. He has lead several academic and industrial Data Mining research projects. DeepSense A solution for data analytics in the ocean economy.

dblp: Luís Torgo

My profile My library Metrics Alerts. Data Mining I CC Potential of dissimilatory nitrate reduction pathways in polycyclic aromatic hydrocarbon degradation. Rule Combination in Inductive Learning. Learning with Imbalanced Domains: Socially Driven News Recommendation. Wind speed forecasting using spatio-temporal indicators. Construction of sentiment classifiers is a standard text mining task, but here we address the ,uis of how to properly evaluate them as there is no settled way to do so.

New citations to this author. Functional Models torbo Regression Tree Leaves. Their combined citations are counted only for the first article. Environmental controls on estuarine nitrifying communities along a salinity gradient. RibeiroBernhard Pfahringer: Access to the Final Selection Minute The access to the final selection minute is only available to applicants. Controlled Redundancy in Incremental Rule Learning.

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Luís Torgo – personal home page

RibeiroBernhard PfahringerPaula Branco: We find no significant difference between the best cross-validation and sequential validation. Nitrolimit Life at the Edge: Resampling strategies are among the most successful approaches to address imbalanced domains. Naphthalene and fluoranthene levels decreased over time with distinct degradation dynamics varying with sediment type.

Please check the confirmation e-mail of your application to obtain the access code. This paper focuses on imbalanced domains in both classification and regression tasks.

Luis Torgo

This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Data Mining with R. Email address for updates.