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Francisco de A. T. de Carvalho
Francisco de Assis Tenório de Carvalho
2010 – today
- 2013
[j19]Francisco de A. T. de Carvalho, Yves Lechevallier, Filipe M. de Melo: Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices. Fuzzy Sets and Systems 215: 1-28 (2013)- 2012
- [j18]Thais G. do Rego, Helge G. Roider, Francisco de A. T. de Carvalho, Ivan G. Costa: Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models. Bioinformatics 28(18): 2297-2303 (2012)
- [j17]Francisco de A. T. de Carvalho, Yves Lechevallier, Filipe M. de Melo: Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognition 45(1): 447-464 (2012)
- [c73]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier, Thierry Despeyroux: Un algorithme de classification automatique pour des données relationnelles multivues. EGC 2012: 125-136
- [c72]Francisco de A. T. de Carvalho, Julio T. Pimentel: A fuzzy clustering algorithm based on adaptive city-block distances. FUZZ-IEEE 2012: 1-8
- [c71]Marcelo R. P. Ferreira, Francisco de A. T. de Carvalho: Kernel fuzzy clustering methods based on local adaptive distances. FUZZ-IEEE 2012: 1-8
- [c70]Valmir Macário Filho, Francisco de A. T. de Carvalho: An adaptive semi-supervised fuzzy clustering algorithm based on objective function optimization. FUZZ-IEEE 2012: 1-8
- [c69]Sérgio R. de M. Queiroz, Francisco de A. T. de Carvalho, Yves Lechevallier: Multicriteria clustering with weighted Tchebycheff distances for relational data. IJCNN 2012: 1-6
- [c68]Francisco de A. T. de Carvalho, Gibson B. N. Barbosa, Marcelo R. P. Ferreira: Variable-Wise Kernel-Based Clustering Algorithms for Interval-Valued Data. SBRN 2012: 25-30
- [c67]Valmir Macário Filho, Ivan G. Costa, João F. L. Oliveira, Francisco de A. T. de Carvalho: Predicting Gene Functions Using Semi-supervised Clustering Algorithms with Objective Function Optimization. SBRN 2012: 61-66
- [c66]Marcelo R. P. Ferreira, Francisco de A. T. de Carvalho: Partitioning hard kernel clustering methods based on local adaptive distances. SMC 2012: 339-344
- [c65]Alberto Pereira de Barros, Francisco de Assis Tenório de Carvalho, Eufrasio de Andrade Lima Neto: A pattern classifier for interval-valued data based on multinomial logistic regression model. SMC 2012: 541-546
- [c64]C. A. G. de Araujo Junior, Francisco de A. T. de Carvalho, André Luis Santiago Maia: Exponential smoothing methods for forecasting bar diagram-valued time series. SMC 2012: 1361-1366
- [c63]Francisco de A. T. de Carvalho, Julio T. Pimentel: Partitioning fuzzy clustering algorithms for interval-valued data based on Hausdorff distances. SMC 2012: 1379-1384
- [c62]Francisco de A. T. de Carvalho, Lucas F. S. Cambuim: Partitioning fuzzy clustering algorithms for mixed feature-type symbolic data. SMC 2012: 1385-1390
- [c61]Valmir Macário Filho, Francisco de A. T. de Carvalho: An adaptive isodata fuzzy clustering algorithm with partial supervision. SMC 2012: 1978-1983
- 2011
- [j16]Ivan G. Costa, Helge G. Roider, Thais G. do Rego, Francisco de A. T. de Carvalho: Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models. BMC Bioinformatics 12(S-1): S29 (2011)
- [j15]Byron Leite Dantas Bezerra, Francisco de Assis Tenório de Carvalho: Symbolic data analysis tools for recommendation systems. Knowl. Inf. Syst. 26(3): 385-418 (2011)
- [c60]Anderson B. dos S. Dantas, Francisco de A. T. de Carvalho: Adaptive Batch SOM for Multiple Dissimilarity Data Tables. ICTAI 2011: 575-578
- [c59]Luciano D. S. Pacifico, Francisco de A. T. de Carvalho: A batch self-organizing maps algorithm based on adaptive distances. IJCNN 2011: 2297-2304
- [i1]Antonio Irpino, Rosanna Verde, Francisco de A. T. de Carvalho: Dynamic Clustering of Histogram Data Based on Adaptive Squared Wasserstein Distances. CoRR abs/1110.1462 (2011)
- 2010
- [j14]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho: Constrained linear regression models for symbolic interval-valued variables. Computational Statistics & Data Analysis 54(2): 333-347 (2010)
- [j13]Francisco de A. T. de Carvalho, Camilo P. Tenorio: Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances. Fuzzy Sets and Systems 161(23): 2978-2999 (2010)
- [j12]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza: Unsupervised pattern recognition models for mixed feature-type symbolic data. Pattern Recognition Letters 31(5): 430-443 (2010)
- [c58]Francisco de Assis Tenório de Carvalho: Recent advances in partitioning clustering algorithms for interval-valued data. EGC 2010: 19-20
- [c57]Valmir Macário Filho, Francisco de Assis Tenório de Carvalho: A new approach for semi-supervised clustering based on Fuzzy C-Means. FUZZ-IEEE 2010: 1-8
- [c56]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier: A relational fuzzy c-means clustering algorithm based on multiple dissimilarity matrices. ISDA 2010: 43-48
- [c55]Clerton Ribeiro, Francisco de Assis Tenório de Carvalho, Ivan G. Costa: Semi-supervised Approach for Finding Cancer Sub-classes on Gene Expression Data. BSB 2010: 25-34
2000 – 2009
- 2009
- [j11]Francisco de A. T. de Carvalho, Yves Lechevallier: Partitional clustering algorithms for symbolic interval data based on single adaptive distances. Pattern Recognition 42(7): 1223-1236 (2009)
- [j10]Francisco de A. T. de Carvalho, Marc Csernel, Yves Lechevallier: Clustering constrained symbolic data. Pattern Recognition Letters 30(11): 1037-1045 (2009)
- [j9]Francisco de A. T. de Carvalho, Yves Lechevallier: Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances. IEEE Transactions on Systems, Man, and Cybernetics, Part A 39(6): 1295-1306 (2009)
- [c54]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho: Vers la simulation et la détection des changements des données évolutives d'usage du Web. EGC 2009: 453-454
- [c53]Rodrigo G. F. Soares, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho: An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data. ICANN (1) 2009: 131-140
- [c52]Eufrasio de Andrade Lima Neto, Gauss Moutinho Cordeiro, Francisco de Assis Tenório de Carvalho, Ulisses Umbelino dos Anjos, Abner Gomes da Costa: Bivariate Generalized Linear Model for Interval-Valued Variables. IJCNN 2009: 2226-2229
- [c51]Kelly P. Silva, Francisco de A. T. de Carvalho, Marc Csernel: Clustering of symbolic data using the assignment-prototype algorithm. IJCNN 2009: 2936-2942
- [p2]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho: Clustering Dynamic Web Usage Data. Innovative Applications in Data Mining 2009: 71-82
- [p1]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho: Comparing Clustering on Symbolic Data. Intelligent Text Categorization and Clustering 2009: 81-94
- 2008
- [j8]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho: Centre and Range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis 52(3): 1500-1515 (2008)
- [c50]André Luis Santiago Maia, Francisco de A. T. de Carvalho: Neural Networks and Exponential Smoothing Models for Symbolic Interval Time Series Processing - Applications in Stock Market. HIS 2008: 326-331
- [c49]Francisco de A. T. de Carvalho, Luciano D. S. Pacifico: A Weighted Partitioning Dynamic Clustering Algorithm for Quantitative Feature Data Based on Adaptive Euclidean Distances. HIS 2008: 398-403
- [c48]Kelly P. Silva, Rodrigo G. F. Soares, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir: Evolving both size and accuracy of RBF networks using Memetic Algorithm. IJCNN 2008: 1938-1944
- [c47]Rodrigo G. F. Soares, Kelly P. Silva, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho: An evolutionary approach for the clustering data problem. IJCNN 2008: 1945-1950
- [c46]Kelly P. Silva, Francisco de A. T. de Carvalho, Marc Csernel: Clustering of symbolic data through a dissimilarity volume based measure. IJCNN 2008: 2865-2871
- [c45]André Luis Santiago Maia, Francisco de A. T. de Carvalho: Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data. SBIA 2008: 207-216
- [c44]Valmir Macário Filho, Ricardo Bastos Cavalcante Prudêncio, Francisco de A. T. de Carvalho, Leandro R. Torres, Laerte Rodrigues Jr., Marcos G. Lima: Automatic Information Extraction in Semi-structured Official Journals. SBRN 2008: 51-56
- [c43]Eufrasio de Andrade Lima Neto, Francisco de Assis Tenório de Carvalho: Nonlinear regression model to symbolic interval-valued variables. SMC 2008: 1247-1252
- 2007
- [j7]Francisco de A. T. de Carvalho: Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognition Letters 28(4): 423-437 (2007)
- [c42]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho: Construction et analyse de résumés de données évolutives : application aux données d'usage du Web. EGC 2007: 539-544
- [c41]Eleonora Ma. Jesus Oliveira, Paulemir G. Campos, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho, Wilson Rosa de Oliveira: Application of a Hybrid Classifier to the Recognition of Petrochemical Odors. HIS 2007: 78-83
- [c40]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho: A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances. HIS 2007: 168-173
- [c39]Camilo P. Tenorio, Francisco de A. T. de Carvalho, Julio T. Pimentel: A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances. HIS 2007: 174-179
- [c38]Francisco de A. T. de Carvalho, Julio T. Pimentel, Lucas X. T. Bezerra: Clustering of symbolic interval data based on a single adaptive L1 distance. IJCNN 2007: 224-229
- [c37]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Jose F. Coelho Neto: Inequality Constraints in Regression Models to Symbolic Interval Variables. IJCNN 2007: 801-806
- [c36]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho: Analyzing Distance Measures for Symbolic Data Based on Fuzzy Clustering. ISDA 2007: 109-114
- [c35]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho: Construction and Analysis of Evolving Data Summaries: An Application on Web Usage Data. ISDA 2007: 377-380
- [c34]Francisco de A. T. de Carvalho, Julio T. Pimentel, Lucas X. T. Bezerra, Renata M. C. R. de Souza: Clustering symbolic interval data based on a single adaptive hausdorff distance. SMC 2007: 451-455
- [c33]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Jose F. Coelho Neto: Constrained linear regression models for interval-valued data with dependence. SMC 2007: 456-461
- 2006
- [j6]Francisco de A. T. de Carvalho, Camilo P. Tenorio, Nicomedes L. Cavalcanti Junior: Partitional fuzzy clustering methods based on adaptive quadratic distances. Fuzzy Sets and Systems 157(21): 2833-2857 (2006)
- [j5]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Marie Chavent, Yves Lechevallier: Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters 27(3): 167-179 (2006)
- [c32]Fabrice Rossi, Francisco de A. T. de Carvalho, Yves Lechevallier, Alzennyr Da Silva: Comparaison de dissimilarité pour l'analyse de l'usage d'un site web. EGC 2006: 409-414
- [c31]Alzennyr Da Silva, Francisco de Assis Tenório de Carvalho, Yves Lechevallier, Brigitte Trousse: Characterizing visitor groups from web data streams. GrC 2006: 389-392
- [c30]Fabio C. D. Silva, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Joyce Q. Silva: A Modal Symbolic Classifier for Interval Data. ICONIP (2) 2006: 50-59
- [c29]André Luis Santiago Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir: A Hybrid Model for Symbolic Interval Time Series Forecasting. ICONIP (2) 2006: 934-941
- [c28]Francisco de A. T. de Carvalho: A Fuzzy Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Euclidean Distance. ICONIP (3) 2006: 1012-1021
- [c27]Gecynalda Soares S. Gomes, André Luis Santiago Maia, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho, Aluizio F. R. Araújo: Hybrid model with dynamic architecture for forecasting time series. IJCNN 2006: 3742-3747
- [c26]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho, Brigitte Trousse: Mining Web Usage Data for Discovering Navigation Clusters. ISCC 2006: 910-915
- [c25]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Daniel F. Pizzato: A Partitioning Method for Mixed Feature-Type Symbolic Data Using a Squared Euclidean Distance. KI 2006: 260-273
- [c24]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Lucas X. T. Bezerra: A dynamical clustering method for symbolic interval data based on a single adaptive Euclidean distance. SBRN 2006: 42-47
- [c23]Francisco de A. T. de Carvalho: Fuzzy clustering algorithms for symbolic interval data based on adaptive and non-adaptive Euclidean distances. SBRN 2006: 60-65
- [c22]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Lucas X. T. Bezerra: Linear Regression Methods to Predict Interval-Valued Data. SBRN 2006: 125-130
- [c21]André Luis Santiago Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir: Symbolic interval time series forecasting using a hybrid model. SBRN 2006: 202-207
- [c20]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Valmir Macário Filho: C^2: : A Collaborative Recommendation System Based on Modal Symbolic User Profile. Web Intelligence 2006: 673-679
- 2005
- [c19]Nicomedes Cavalcanti, Francisco de A. T. de Carvalho: An Adaptive Fuzzy c-Means Algorithm with the L2 Norm. Australian Conference on Artificial Intelligence 2005: 1138-1141
- [c18]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Eduarda S. Freire: Applying Constrained Linear Regression Models to Predict Interval-Valued Data. KI 2005: 92-106
- [c17]Luciano Barbosa, Ana Carolina Salgado, Francisco de A. T. de Carvalho, Jacques Robin, Juliana Freire: Looking at both the present and the past to efficiently update replicas of web content. WIDM 2005: 75-80
- 2004
- [j4]Byron L. D. Bezerra, Francisco de A. T. de Carvalho: A symbolic approach for content-based information filtering. Inf. Process. Lett. 92(1): 45-52 (2004)
- [j3]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho: Clustering of interval data based on city-block distances. Pattern Recognition Letters 25(3): 353-365 (2004)
- [j2]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho: A Modal Symbolic Classifier for selecting time series models. Pattern Recognition Letters 25(8): 911-921 (2004)
- [c16]Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Camilo P. Tenorio: Univariate and Multivariate Linear Regression Methods to Predict Interval-Valued Features. Australian Conference on Artificial Intelligence 2004: 526-537
- [c15]Byron L. D. Bezerra, Francisco de A. T. de Carvalho: A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. Australian Conference on Artificial Intelligence 2004: 1011-1016
- [c14]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Gustavo Alves: Collaborative Filtering Based on Modal Symbolic User Profiles: Knowing You in the First Meeting. IBERAMIA 2004: 235-245
- [c13]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Camilo P. Tenorio: Two Partitional Methods for Interval-Valued Data Using Mahalanobis Distances. IBERAMIA 2004: 454-463
- [c12]Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza: A Classifier for Quantitative Feature Values Based on a Region Oriented Symbolic Approach. IBERAMIA 2004: 464-473
- [c11]Alzennyr Da Silva, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir, Nicomedes Cavalcanti: Comparing Metrics in Fuzzy Clustering for Symbolic Data on SODAS Format. IBERAMIA 2004: 727-736
- [c10]Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza: Classification of SAR Images Through a Convex Hull Region Oriented Approach. ICONIP 2004: 769-774
- [c9]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Fabio C. D. Silva: Clustering of Interval-Valued Data Using Adaptive Squared Euclidean Distances. ICONIP 2004: 775-780
- [c8]Francisco de A. T. de Carvalho, Eufrasio de A. Lima Neto, Camilo P. Tenorio: A New Method to Fit a Linear Regression Model for Interval-Valued Data. KI 2004: 295-306
- [c7]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Fabio C. D. Silva: A Clustering Method for Symbolic Interval-Type Data Using Adaptive Chebyshev Distances. SBIA 2004: 266-275
- [c6]Sérgio R. de M. Queiroz, Francisco de A. T. de Carvalho: Making Collaborative Group Recommendations Based on Modal Symbolic Data. SBIA 2004: 307-316
- 2002
- [j1]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto: Comparative study on proximity indices for cluster analysis of gene expression time series. Journal of Intelligent and Fuzzy Systems 13(2-4): 133-142 (2002)
- [c5]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Geber Ramalho, Jean-Daniel Zucker: Speeding up Recommender Systems with Meta-prototypes. SBIA 2002: 227-236
- [c4]Ivan R. Teixeira, Francisco de A. T. de Carvalho, Geber Ramalho, Vincent Corruble: ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems. SBIA 2002: 237-247
- [c3]Sérgio R. de M. Queiroz, Francisco de A. T. de Carvalho, Geber Ramalho, Vincent Corruble: Making Recommendations for Groups Using Collaborative Filtering and Fuzzy Majority. SBIA 2002: 248-258
- [c2]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto: A Symbolic Approach to Gene Expression Time Series Analysis. SBRN 2002: 25-30
- [c1]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto: Stability Evaluation of Clustering Algorithms for Time Series Gene Expression Data. WOB 2002: 88-90
Coauthor Index
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last updated on 2013-03-12 21:36 CET by the dblp team