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Ricardo B. C. Prudêncio
Researcher at Federal University of Pernambuco
Publications - 118
Citations - 1979
Ricardo B. C. Prudêncio is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Meta learning (computer science) & Support vector machine. The author has an hindex of 20, co-authored 106 publications receiving 1608 citations. Previous affiliations of Ricardo B. C. Prudêncio include Universidade de Pernambuco.
Papers
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Journal ArticleDOI
A multiple kernel learning algorithm for drug-target interaction prediction.
TL;DR: KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks, allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size.
Journal ArticleDOI
Meta-learning approaches to selecting time series models
TL;DR: An original work that applies meta-learning approaches to select models for time-series forecasting and uses the NOEMON approach to rank three models used to forecast time series of the M3-Competition.
Journal ArticleDOI
Combining meta-learning and search techniques to select parameters for support vector machines
Taciana A. F. Gomes,Ricardo B. C. Prudêncio,Carlos Soares,André L. Rossi,André C. P. L. F. de Carvalho +4 more
TL;DR: This work investigates the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search and showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.
Proceedings ArticleDOI
Supervised link prediction in weighted networks
TL;DR: The aim is to investigate the relevance of using weights to improve supervised link prediction, and the preliminary results on supervised prediction on a co-authorship network revealed satisfactory results when weights were considered.
Proceedings ArticleDOI
Time Series Based Link Prediction
TL;DR: The preliminary results using two link prediction methods (unsupervised and supervised) on co-authorship networks revealed satisfactory results when temporal information was considered, and a forecasting model was deployed on these time series and used as the final scores of the pairs.