Ricardo Bastos C. Prudencio
Ricardo Bastos C. Prudencio
Centro de Informática - UFPE - Brasil
Verified email at cin.ufpe.br - Homepage
Cited by
Cited by
Combining meta-learning and search techniques to select parameters for support vector machines
TAF Gomes, RBC Prudêncio, C Soares, ALD Rossi, A Carvalho
Neurocomputing 75 (1), 3-13, 2012
Meta-learning approaches to selecting time series models
RBC Prudêncio, TB Ludermir
Neurocomputing 61, 121-137, 2004
Supervised link prediction in weighted networks
HR De Sá, RBC Prudêncio
The 2011 international joint conference on neural networks, 2281-2288, 2011
A multiple kernel learning algorithm for drug-target interaction prediction
ACA Nascimento, RBC Prudêncio, IG Costa
BMC bioinformatics 17 (1), 1-16, 2016
Time series based link prediction
PR da Silva Soares, RBC Prudêncio
The 2012 international joint conference on neural networks (IJCNN), 1-7, 2012
Ranking and selecting clustering algorithms using a meta-learning approach
MCP De Souto, RBC Prudencio, RGF Soares, DSA De Araujo, IG Costa, ...
2008 IEEE International Joint Conference on Neural Networks (IEEE World …, 2008
A literature review of recommender systems in the television domain
D Véras, T Prota, A Bispo, R Prudêncio, C Ferraz
Expert Systems with Applications 42 (22), 9046-9076, 2015
Proximity measures for link prediction based on temporal events
PRS Soares, RBC Prudêncio
Expert Systems with Applications 40 (16), 6652-6660, 2013
Making sense of item response theory in machine learning
F Martínez-Plumed, RBC Prudêncio, A Martínez-Usó, J Hernández-Orallo
ECAI 2016, 1140-1148, 2016
A multi-objective particle swarm optimization for test case selection based on functional requirements coverage and execution effort
LS de Souza, PBC de Miranda, RBC Prudencio, FA Barros
2011 IEEE 23rd International Conference on Tools with Artificial …, 2011
A hybrid meta-learning architecture for multi-objective optimization of SVM parameters
PBC Miranda, RBC Prudêncio, APLF De Carvalho, C Soares
Neurocomputing 143, 27-43, 2014
Using machine learning techniques to combine forecasting methods
R Prudêncio, T Ludermir
Australasian Joint Conference on Artificial Intelligence, 1122-1127, 2004
A modal symbolic classifier for selecting time series models
RBC Prudêncio, TB Ludermir, FAT de Carvalho
Pattern Recognition Letters 25 (8), 911-921, 2004
Item response theory in AI: Analysing machine learning classifiers at the instance level
F Martínez-Plumed, RBC Prudêncio, A Martínez-Usó, J Hernández-Orallo
Artificial Intelligence 271, 18-42, 2019
Good to be bad? Distinguishing between positive and negative citations in scientific impact
DC Cavalcanti, RBC Prudêncio, SS Pradhan, JY Shah, RS Pietrobon
2011 IEEE 23rd International Conference on Tools with Artificial …, 2011
Data complexity meta-features for regression problems
AC Lorena, AI Maciel, PBC de Miranda, IG Costa, RBC Prudêncio
Machine Learning 107 (1), 209-246, 2018
Empirical investigation of active learning strategies
D Pereira-Santos, RBC Prudêncio, AC de Carvalho
Neurocomputing 326, 15-27, 2019
Search based constrained test case selection using execution effort
LS De Souza, RBC Prudêncio, FA Barros, EHS Aranha
Expert systems with applications 40 (12), 4887-4896, 2013
Mining rules for the automatic selection process of clustering methods applied to cancer gene expression data
ACA Nascimento, RBC Prudêncio, MCP De Souto, IG Costa
International Conference on Artificial Neural Networks, 20-29, 2009
Selecting machine learning algorithms using the ranking meta-learning approach
RBC Prudêncio, MCP De Souto, TB Ludermir
Meta-learning in computational intelligence, 225-243, 2011
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