Pattern recognition in Latin America in the "big data" era

Pattern Recognition

Volumn 48, Issue 4, 2015, Pages 1185-1196

  Fernández, Alicia ^a   Gómez, Álvaro ^a   Lecumberry, Federico ^a   Pardo, Álvaro ^b   Ramírez, Ignacio ^a  

^a UNIVERSIDAD DE LA REPÚBLICA   (Uruguay)

^b UNIVERSIDAD CATÓLICA DEL URUGUAY   (Uruguay)

Author keywords

Big Data; Computer vision; Data mining; High dimension; Image analysis; Large scale; Latin America; Machine learning; Pattern Recognition

Indexed keywords

BIG DATA; COMPUTER VISION; DIGITAL STORAGE; IMAGE ANALYSIS; LEARNING SYSTEMS; PATTERN RECOGNITION;

HETEROGENEOUS DATA; HIGH DIMENSIONS; LARGE SCALE; LATIN AMERICA; LATIN AMERICANS; ONLINE TRANSACTION; PRIVATE ORGANIZATIONS; RESEARCH INSTITUTES;

DATA MINING;

EID: 84920707160     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.04.012     Document Type: Article

References (161)

1. N. Acosta Mendoza, A. Gago-Alonso, J. Medina-Pagola, On speeding up frequent approximate subgraph mining, in: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 7441, 2012, pp. 316-323.
2. N. Acosta-Mendoza, A. Gago-Alonso, J.E. Medina-Pagola, Frequent approximate subgraphs as features for graph-based image classification, Knowl. Based Syst. 27 (2012) 381-392.
3. C. Aguerrebere, P. Sprechmann, P. Musé, R. Ferrando, A-contrario localization of epileptogenic zones in SPECT images, in: 6th IEEE International Symposium on Biomedical Imaging (ISBI 09), Boston, MA, USA, 28 June-1 July 2009, pp. 1-4.
4. J. Almeida, N. Leite, R. Torres, Vison: video summarization for online applications, Pattern Recognit. Lett. 33 (4) (2012) 397-409.
5. A. Alvarez-Ellacuria, A. Orfila, L. Gómez-Pujol, G. Simarro, N. Obregon, Decoupling spatial and temporal patterns in short-term beach shoreline response to wave climate, Geomorphology 128 (3-4) (2011) 199-208.
6. A. Alvarez-Meza, G. Daza-Santacoloma, G. Castellanos-Dominguez, Biomedical data analysis by supervised manifold learning, in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012, pp. 41-44.
7. H. Anaya-Sánchez, A. Pons-Porrata, R. Berlanga-Llavori, A new document clustering algorithm for topic discovering and labeling, in: Lecture Notes in Computer Science, vol. 5197, 2008, pp. 161-168.
8. H. Anaya-Sánchez, A. Pons-Porrata, R. Berlanga-Llavori, A document clustering algorithm for discovering and describing topics, Pattern Recognit. Lett. 31 (6) (2010) 502-510.
9. J.D. Arias-Londoño, J.I. Godino-Llorente, N. Sáeenz-Lechón, V. Osma-Ruiz, G. Castellanos-Domínguez, An improved method for voice pathology detection by means of a HMM-based feature space transformation, Pattern Recognit. 43 (9) (2010) 3100-3112.
10. T. Arredondo, D. Candel, M. Leiva, L. Dombrovskaia, L. Agulló, M. Seeger, Inference system using softcomputing and mixed data applied in metabolic pathway datamining, Int. J. Data Min. Bioinform. 6 (1) (2012) 61-85.
11. F. Artigas-Fuentes, R. Gil-García, J. Badía-Contelles, A high-dimensional access method for approximated similarity search in text mining, in: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 3155-3158.
12. F. Artigas-Fuentes, R. Gil-García, J. Badía-Contelles, A. Pons-Porrata, Fast k-nn classi fier for documents based on a graph structure, in: Lecture Notes in Computer Science, vol. 6419, 2010, pp. 228-235.
13. C. Astudillo, B. Oommen, On achieving semi-supervised pattern recognition by utilizing tree-based SOMs, Pattern Recognit. 46 (1) (2013) 293-304.
14. L.D. Avendaño-Valencia, J. Martínez-Vargas, E. Giraldo, G. Castellanos-Domínguez, Reduction of irrelevant and redundant data from TFRs for EEG signal classification, in: Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, 2010, pp. 4010-4013.
15. S. Avila, N. Thome, M. Cord, E. Valle, A. de A. Araújo, Pooling in image representation: the visual codeword point of view, Comput. Vis. Image Underst. 117 (5) (2013) 453-465.
16. M.C.N. Barioni, H.L. Razente, A.J. Traina, C. T Jr., Accelerating k-medoid-based algorithms through metric access methods, J. Syst. Softw. 81 (3) (2008) 343-355.
17. A. Bartesaghi, P. Sprechmann, J. Liu, G. Randall, G. Sapiro, S. Subramaniam, Classification and 3D averaging with missing wedge correction in biological electron tomography, J. Struct. Biol. 162 (3) (2008) 436-450.
18. N.C. Batista, A.P.B. Lopes, A.d.A. Araújo, Detecting buildings in historical photographs using bag-of-keypoints, in: Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI '09, IEEE Computer Society, Washington, DC, USA, 2009, pp. 276-283.
19. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Neural Information and Processing Systems, vol. 14, 2001, pp. 585-591.
20. S. Berretti, A. Del Bimbo, P. Pala, F. Silva Mata, Face recognition by SVMS classification of 2d and 3d radial geodesics, in: IEEE International Conference on Multimedia and Expo, 2008, pp. 93-96.
21. C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Inc., New York, NY, USA, 1995.
22. A. Bolivar-Cime, J. Marron, Comparison of binary discrimination methods for high dimension low sample size data, J. Multivar. Anal. 115 (2013) 108-121.
23. P. Bonissone, J.M. Cadenas, M.C. Garrido, R.A. Díaz-Valladares, A fuzzy random forest, Int. J. Approx. Reason. 51 (7) (2010) 729-747.
24. H.B. Borges, J.C. Nievola, Comparing the dimensionality reduction methods in gene expression databases, Expert Syst. Appl. 39 (12) (2012) 10780-10795.
25. L. Breiman, Random forests, Mach. Learn. 45 (1) (2001) 5-32.
26. P. Bugatti, M. Ribeiro, A. Traina, C. Traina, Feature selection guided by perception in medical CBIR systems, in: First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB), 2011, pp. 323-330.
27. P. Bühlmann, B. Yu, Boosting with the l2 loss: regression and classification, J. Am. Stat. Assoc. 98 (462) (2003) 324-339.
28. B. Bustos, G. Navarro, Improving the space cost of k-NN search in metric spaces by using distance estimators, Multimed. Tools Appl. 41 (2) (2009) 215-233.
29. J. Camargo, F. Gonzalez, Multimodal image collection summarization using non-negative matrix factorization, in: 2011 6th Colombian Computing Congress (CCC), 2011, pp. 1-6.
30. J.E. Camargo, J.C. Caicedo, F.A. Gonzalez, A kernel-based framework for image collection exploration, J. Vis. Lang. Comput. 24 (1) (2013) 53-67.
31. G. Capdehourat, A. Corez, A. Bazzano, R. Alonso, P. Musé, Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions, Pattern Recognit. Lett. 32 (16) (2011) 2187-2196.
32. A. Castaño, F. Fernández-Navarro, C. Hervás-Martínez, P. Gutierrez, Neurologistic models based on evolutionary generalized radial basis function for the microarray gene expression classification problem, Neural Process. Lett. 34 (2) (2011) 117-131.
33. J. Cervantes, X. Li, W. Yu, K. Li, Support vector machine classi fication for large data sets via minimum enclosing ball clustering, Neurocomputing 71 (4) (2008) 611-619.
34. A.L. Chau, X. Li, W. Yu, Support vector machine classification for large datasets using decision tree and Fisher linear discriminant, Future Gener. Comput. Syst. 36 (2014) 57-65.
35. E. Chavez Gonzalez, K. Figueroa, G. Navarro, Effective proximity retrieval by ordering permutations, IEEE Trans. Pattern Anal. Mach. Intell. 30 (9) (2008) 1647-1658.
36. H. Chernoff, A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations, Ann. Math. Stat. 23 (4) (1952) 493-507.
37. G.G. Chowdhury, Natural language processing, Annu. Rev. Inf. Sci. Technol. 37 (1) (2003) 51-89.
38. M.L. Clark, T.M. Aide, H.R. Grau, G. Riner, A scalable approach to mapping annual land cover at 250 m using MODIS time series data: a case study in the Dry Chaco ecoregion of South America, Remote Sens. Environ. 114 (11) (2010) 2816-2832.
39. S. Cleger-Tamayo, J.M. Fernandez-Luna, J.F. Huete, Explaining neighborhoodbased recommendations, in: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '12, ACM, New York, NY, USA, 2012, pp. 1063-1064.
40. J. Cole, S. Newman, F. Foertter, I. Aguilar, M. Coffey, Breeding and genetics symposium: really big data: processing and analysis of very large data sets, J. Anim. Sci. 90 (3) (2012) 723-733.
41. C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20 (September (3)) (1995) 273-297.
42. Y. Couce, L. Franco, D. Urda, J. Subirats, J. Jerez, Hybrid (generalizationcorrelation) method for feature selection in high dimensional dna microarray prediction problems, in: Lecture Notes in Computer Science, vol. 6692, Part 2, 2011, pp. 202-209.
43. A. Cruz-Roa, J.C. Caicedo, F.A. González, Visual pattern mining in histology image collections using bag of features, Artif. Intell. Med. 52 (2) (2011) 91-106.
44. R. da Costa, A. Gonzaga, Dynamic features for iris recognition, IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42 (4) (2012) 1072-1082.
45. A. da Luz, E. Valle, A. de A Araujo, A context-aware description for content filtering on video sharing social networks, in: IEEE International Conference on Multimedia and Expo (ICME), 2012, pp. 729-734.
46. A. Da Silva, J. Dos Santos, A. Falcão, R. Torres, L. Magalhães, Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning, Comput. Vis. Image Underst. 116 (4) (2012) 510-523.
47. A. Da Silva, A. Falcão, L. Magalhães, Active learning paradigms for CBIR systems based on optimum-path forest classification, Pattern Recognit. 44 (12) (2011) 2971-2978.
48. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, PA, June 1992.
49. G. Daza-Santacoloma, C.D. Acosta-Medina, G. Castellanos-Domínguez, Regularization parameter choice in locally linear embedding, Neurocomputing 73 (10-12) (2010) 1595-1605.
50. G. Daza-Santacoloma, G. Castellanos-Dominguez, J. Principe, Functional data representation using correntropy locally linear embedding, in: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2010, pp. 7-12.
51. F. De la Torre, M.J. Black, Robust principal component analysis for computer vision, in: Proceedings. Eighth IEEE International Conference on Computer Vision, 2001, ICCV 2001, vol. 1, IEEE, 2001, pp. 362-369.
52. J. de Oliveira, E. da Silva, M. Cardoso, A. Hollanda, A face-based authentication system using correlation filters on videos, in: MMSP '09. IEEE International Workshop on Multimedia Signal Processing, 2009, pp. 1-6.
53. A. Desolneux, L. Moisan, J.-M. Morel, A grouping principle and four applications, IEEE Trans. Pattern Anal. Mach. Intell. 25 (4) (2003) 508-513.
54. M. Di Martino, A. Fernández, P. Iturralde, F. Lecumberry, Novel classifier scheme for imbalanced problems, Pattern Recognit. Lett. 34 (10) (2013) 1146-1151.
55. M. Di Martino, G. Hernández, M. Fiori, A. Fernández, A new framework for optimal classi fi er design, Pattern Recognit. 46 (8) (2013) 2249-2255.
56. E. Dorileo, M. Frade, A.M.F. Roselino, R. Rangayyan, P.M. Azevedo-Marques, Color image processing and content-based image retrieval techniques for the analysis of dermatological lesions, in: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, 2008, pp. 1230-1233.
57. J. Dos Santos, P.-H. Gosselin, S. Philipp-Foliguet, R.DaS. Torres, A. Falcão, Multiscale classification of remote sensing images, IEEE Trans. Geosci. Remote Sens. 50 (10 (Part1)) (2012) 3764-3775.
58. J. dos Santos, P.-H. Gosselin, S. Philipp-Foliguet, R. Torres, A. Falcao, Interactive multiscale classification of high-resolution remote sensing images, IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., 2013.
59. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, John Wiley & Sons, New York, NY, 2012.
60. R.P.W. Duin, E. Pkalska, The dissimilarity space: bridging structural and statistical pattern recognition, Pattern Recognit. Lett. 33 (May (7)) (2012) 826-832.
61. H. Fernandes, X. Maldague, M. Batista, C.A.Z. Barcelos, Suspicious event recognition using infrared imagery, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2011, pp. 2186-2191.
62. L.A. Fernandes, M.M. Oliveira, A general framework for subspace detection in unordered multidimensional data, Pattern Recognit. 45 (9) (2012) 3566-3579.
63. J.M. Fernández-Luna, J.F. Huete, H. Rodríguez-Ávila, J.C. Rodríguez-Cano, Proposal of a distributed events model for the development of collaborative search systems, in: Proceedings of the 13th International Conference on Interacción Persona-Ordenador, INTERACCION '12, ACM, New York, NY, USA, 2012, pp. 43:1-43:2.
64. R. Fraiman, A. Justel, M. Svarc, Pattern recognition via projection-based {NN} rules, Comput. Stat. Data Anal. 54 (5) (2010) 1390-1403.
65. R. Fraiman, B. Pateiro-López, Quantiles for finite and infinite dimensional data, J. Multivar. Anal. 108 (2012) 1-14.
66. A. Franco-Arcega, J. Carrasco-Ochoa, G. Sánchez-Díaz, J.F. Martínez-Trinidad, Decision tree induction using a fast splitting attribute selection for large datasets, Expert Syst. Appl. 38 (11) (2011) 14290-14300.
67. A. Franco-Arcega, J. Carrasco-Ochoa, G. Sánchez-Díaz, J.F. Martínez-Trinidad, Building fast decision trees from large training sets, Intell. Data Anal. 16 (4) (2012) 649-664.
68. A. Gago-Alonso, J. Carrasco-Ochoa, J. Medina-Pagola, J. Martínez-Trinidad, Full duplicate candidate pruning for frequent connected subgraph mining, Integr. Comput. Aided Eng. 17 (3) (2010) 211-225.
69. A. Gago-Alonso, A. Puentes-Luberta, J. Carrasco-Ochoa, J. Medina-Pagola, J. Martínez-Trinidad, A new algorithm for mining frequent connected subgraphs based on adjacency matrices, Intell. Data Anal. 14 (3) (2010) 385-403.
70. G.I. Gavier-Pizarro, T. Kuemmerle, L.E. Hoyos, S.I. Stewart, C.D. Huebner, N. S. Keuler, V.C. Radeloff, Monitoring the invasion of an exotic tree ( Ligustrum lucidum) from 1983 to 2006 with landsat TM/ETM + satellite data and support vector machines in córdoba, argentina, Remote Sens. Environ. 122 (2012) 134-145.
71. L.G.S. Giraldo, G.C. Domínguez,Weighted feature extraction with a functional data extension, Neurocomputing 73 (10-12) (2010) 1760-1773.
72. J. Goddard, G. Schlotthauer, M. Torres, H. Rufiner, Dimensionality reduction for visualization of normal and pathological speech data, Biomed. Signal Process. Control 4 (3) (2009) 194-201.
73. O. González-Recio, K. Weigel, D. Gianola, H. Naya, G. Rosa, L2-boosting algorithm applied to high-dimensional problems in genomic selection, Genet. Res. 92 (3) (2010) 227-237.
74. G. Grinblat, L. Uzal, H. Ceccatto, P. Granitto, Solving nonstationary classification problems with coupled support vector machines, IEEE Trans. Neural Netw. 22 (1) (2011) 37-51.
75. D. Guimarães Pedronette, R.da S. Torres, Image re-ranking and rank aggregation based on similarity of ranked lists, Pattern Recognit. 46 (8) (2013) 2350-2360.
76. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn. 46 (1-3) (2002) 389-422, Cited By (since 1996):2223.
77. G. Haro, G. Randall, G. Sapiro, Translated poisson mixture model for stratifi cation learning, Int. J. Comput. Vis. 80 (3) (2008) 358-374.
78. J. Hernandez, J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad, An empirical study of oversampling and undersampling for instance selection methods on imbalance datasets, in: J. Ruiz-Shulcloper, G. Sanniti di Baja (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, vol. 8258, Springer, Berlin, Heidelberg, 2013, pp. 262-269.
79. N. Hernández, Y. Martínez-Díaz, D. Porro-Muñoz, H. Méndez-Vázquez, Face recognition: Would going back to functional nature be a good idea?, in: Lecture Notes in Computer Science, vol. 7441, 2012, pp. 98-105.
80. M. Hesketh, G.A. Sánchez-Azofeifa, The effect of seasonal spectral variation on species classification in the panamanian tropical forest, Remote Sens. Environ. 118 (2012) 73-82.
81. P. Iturralde, M. Patrone, F. Lecumberry, A. Fernández, Motor intention recognition in eeg: In pursuit of a relevant feature set, in: Lecture Notes in Computer Science, vol. 7441, 2012, pp. 551-558.
82. B.-H. Juang, Hidden Markov models, Encyclopedia of Telecommunications, 1985.
83. B. Julesz, Textons, the elements of texture perception, and their interactions, Nature 290 (March) (1981) 91-97.
84. T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cybern. 43 (1) (1982) 59-69.
85. T.S. Korting, L.M.G. Fonseca, G. Camara, Geodma-geographic data mining analyst, Comput. Geosci. 57 (2013) 133-145.
86. A. Kuri-Morales, F. Rodríguez-Erazo, A search space reduction methodology for data mining in large databases, Eng. Appl. Artif. Intell. 22 (1) (2009) 57-65.
87. D. Laney, 3d Data Management: Controlling data volume, Velocity, and Variety, Application Delivery Strategies, META Group Inc, Stamford, CT, 2001.
88. F. Lecumberry, A. Pardo, G. Sapiro, Simultaneous object classification and segmentation with high-order multiple shape models, IEEE Trans. Image Process. 19 (3) (2010) 625-635.
89. R. León, A. Suárez, C. Uribe, Z. Zavaleta, An algorithm for mining frequent itemsets, in: 5th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2008, 2008, pp. 334-339.
90. A.L. Levada, N.D. Mascarenhas, A. Tannús, A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms, Pattern Recognit. Lett. 31 (13) (2010) 1795-1808.
91. X. Li, J. Cervantes, W. Yu, A novel SVM classification method for large data sets, in: Proceedings of the 2010 IEEE International Conference on Granular Computing, GRC '10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 297-302.
92. S. Lloyd, Least squares quantization in PCM, IEEE Trans. Inf. Theory 28 (2) (1982) 129-137.
93. A. López-Chau, J. Cervantes, L. López-García, F.G. Lamont, Fisher's decision tree, Expert Syst. Appl. 40 (16) (2013) 6283-6291.
94. L. López-Kleine, J. Romeo, F. Torres-Avilés, Gene functional prediction using clustering methods for the analysis of tomato microarray data, Adv. Intell. Syst. Comput. 222 (2013) 1-6.
95. D. Lowe, Object recognition from local scale-invariant features, in: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, 1999, pp. 1150-1157.
96. A. Álvarez Meza, J. Valencia-Aguirre, G. Daza-Santacoloma, C. Acosta-Medina, G. Castellanos-Domínguez, Video analysis based on multi-kernel representation with automatic parameter choice, Neurocomputing 100 (2013) 117-126.
97. A. Álvarez Meza, J. Valencia-Aguirre, G. Daza-Santacoloma, G. Castellanos- Domínguez, Global and local choice of the number of nearest neighbors in locally linear embedding, Pattern Recognit. Lett. 32 (16) (2011) 2171-2177.
98. S. Maldonado, R. Weber, A wrapper method for feature selection using support vector machines, Inf. Sci. 179 (13) (2009) 2208-2217.
99. M. Mello, C. Vieira, B. Rudorff, P. Aplin, R. Santos, D. Aguiar, Stars: a new method for multitemporal remote sensing, IEEE Trans. Geosci. Remote Sens. 51 (4) (2013) 1897-1913.
100. A. Mesa, C. Feregrino-Uribe, R. Cumplido, J. Hernández-Palancar, A highly parallel algorithm for frequent itemset mining, in: Lecture Notes in Computer Science, vol. 6256, 2010, pp. 291-300.
101. A. Morales-González, E. García-Reyes, Assessing the role of spatial relations for the object recognition task, in: Lecture Notes in Computer Science, vol. 6419, 2010, pp. 549-556.
102. A. Morales-González, E. García-Reyes, Simple object recognition based on spatial relations and visual features represented using irregular pyramids, in: Multimedia Tools and Applications, 2011, pp. 1-23.
103. A. Mottini, F. Miceli, G. Albín, C. Aguerrebere, A. Fernández, M. Núñez, R. Ferrando, Integrated software for the detection of epileptogenic zones in refractory epilepsy, in: IEEE Engineering in Medicine and Biology Annual Conference: Proceedings, 2010.
104. M. Naldi, R. Campello, Evolutionary k-means for distributed data sets, Neurocomputing 127 (2013) 30-42.
105. M. Naldi, R. Campello, E. Hruschka, A. Carvalho, Efficiency issues of evolutionary k-means, Appl. Soft Comput. 11 (March (2)) (2011) 1938-1952.
106. L. Neves, G. Giraldi, SVM framework for incorporating content-based image retrieval and data mining into the SBIM image manager, in: J.A.M.R. Tavares, R.M. Natal Jorge (Eds.), Topics in Medical Image Processing and Computational Vision, Lecture Notes in Computational Vision and Biomechanics, vol. 8, Springer, Netherlands, 2013, pp. 49-66.
107. L.A.P. Neves, G.M. Porcides, G.A. Giraldi, Sbim: an internet manager for sharing biological and medical images, J. Health Inf. 3 (4) (2011).
108. M. Pamplona Segundo, L. Silva, O.R.P. Bellon, Real-time scale-invariant face detection on range images, in: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2011, pp. 914-919.
109. J. Papa, F. Cappabianco, A. Falcao, Optimizing optimum-path forest classification for huge datasets, in: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 4162-4165.
110. J. Papa, A. Falcao, P. Miranda, C. Suzuki, N. Mascarenhas, Design of robust pattern classifiers based on optimum-path forests, in: Mathematical Morphology and its Applications to Image and Signal Processing (ISMM 07), 2007, pp. 337-348.
111. C. Passarinho, E.O.T. Salles, M. Sarcinelli Filho, Face detection based on adaptive support vector tracker, in: Biosignals and Biorobotics Conference (BRC), 2011 ISSNIP, 2011, pp. 1-6.
112. K. Pearson, On lines and planes of closest fit to systems of points in space, Philos. Mag. 2 (11) (1901) 559-572.
113. A. Peña-Ayala, Educational data mining: a survey and a data mining-based analysis of recent works, Expert Syst. Appl. 41 (4 (Part 1)) (2013) 1432-1462.
114. O.A. Penatti, E. Valle, R.daS. Torres, Comparative study of global color and texture descriptors for web image retrieval, J. Vis. Commun. Image Represent. 23 (2) (2012) 359-380.
115. L. Pereira, H. Pinheiro, J. Silva, A. Silva, T. Pina, G.D.C. Cavalcanti, T.I. Ren, J. de Oliveira, A fingerprint spoof detection based on MLP and SVM, in: The 2012 International Joint Conference on Neural Networks (IJCNN), 2012, pp. 1-7.
116. A. Pérez-Suárez, J. Martínez-Trinidad, J. Carrasco-Ochoa, J. Medina-Pagola, An algorithm based on density and compactness for dynamic overlapping clustering, Pattern Recognit. 46 (11) (2013) 3040-3055.
117. A. Pérez-Suárez, J. Martínez-Trinidad, J. Carrasco-Ochoa, J. Medina-Pagola, Oclustr: a new graph-based algorithm for overlapping clustering, Neurocomputing 121 (2013) 234-247.
118. F. Perronnin, C. Dance, Fisher kernels on visual vocabularies for image categorization, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR'07, IEEE, 2007, pp. 1-8.
119. R. Pisani, P. Riedel, K. Costa, R. Nakamura, C. Pereira, G. Rosa, J. Papa, Automatic landslide recognition through optimum-path forest, in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012, pp. 6228-6231.
120. Y. Plasencia-Calaña, E. García-Reyes, M. Orozco-Alzate, R. Duin, Prototype selection for dissimilarity representation by a genetic algorithm, in: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 177-180.
121. D. Porro-Muñoz, I. Talavera, R. Duin, M. Orozco-Alzate, J. Londoño-Bonilla, Classifying three-way seismic volcanic data by dissimilarity representation, in: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 814-817.
122. R. Ramos-Pollan, A. Cruz-Roa, F. Gonzalez, A framework for high performance image analysis pipelines, in: 2012 7th Colombian Computing Congress (CCC), 2012, pp. 1-6.
123. A. Reis, A. Silva, A. Paiva, Face detection using Shannon diversity index, in: 18th International Conference on Systems, Signals and Image Processing (IWSSIP), 2011, pp. 1-4.
124. P. Rivas-Perea, J. Cota-Ruiz, An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods, Pattern Recognit. Lett. 34 (4) (2013) 439-451.
125. A. Rodríguez-González, J. Martínez-Trinidad, J. Carrasco-Ochoa, J. Ruiz- Shulcloper, Rp-miner: A relaxed prune algorithm for frequent similar pattern mining, Knowl. Inf. Syst. 27 (3) (2011) 451-471.
126. A. Rodríguez-González, J. Martínez-Trinidad, J. Carrasco-Ochoa, J. Ruiz- Shulcloper, Mining frequent patterns and association rules using similarities, Expert Syst. Appl. 40 (17) (2013) 6823-6836.
127. D. Rosa-Paz, R. Pérez-Vázquez, J. Fernández-Luna, J. Huete, Information retrieval from heterogeneous data sources: an application for managing medical records, in: Communications in Computer and Information Science, CCIS (PART 3), vol. 221, 2011, pp. 146-155.
128. S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290 (5500) (2000) 2323-2326.
129. H. Rue, L. Held, Gaussian Markov Random Fields: Theory and Applications, CRC Press, Boca Raton, FL, 2005.
130. L. Rueda, M. Herrera, Linear dimensionality reduction by maximizing the Chernoff distance in the transformed space, Pattern Recognit. 41 (10) (2008) 3138-3152.
131. M. Sánchez, M. Moreno, S. Segrera, V. López, Framework for the development of a personalised recommender system with integrated web-mining functionalities, Int. J. Comput. Appl. Technol. 33 (4) (2008) 312-319.
132. G. Sánchez-Díaz, J. Ruiz-Shulcloper, A clustering method for very large mixed data sets, in: N. Cercone, T.Y. Lin, X. Wu (Eds.), ICDM, IEEE Computer Society, 2001, pp. 643-644.
133. A. Santos, A. de Albuquerque Araujo, D. Menotti, Combining multiple classification methods for hyperspectral data interpretation, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 6 (3) (2013) 1450-1459.
134. C. Santos, E. dos Santos, E. Souto, Nudity detection based on image zoning, in: 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, pp. 1098-1103.
135. R.E. Schapire, The Strength of Weak Learnability, 1990.
136. B. Scholkopf, A.J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning), 1st ed., The MIT Press, December 2001.
137. W.R. Schwartz, H. Guo, J. Choi, L.S. Davis, Face identification using large feature sets, IEEE Trans. Image Process. 21 (4) (2012) 2245-2255.
138. J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 22 (1997) 888-905.
139. T. Silva, L. Zhao, Stochastic competitive learning in complex networks, IEEE Trans. Neural Netw. Learn. Syst. 23 (3) (2012) 385-398.
140. T. Silva, L. Zhao, Uncovering overlapping cluster structures via stochastic competitive learning, Inf. Sci. 247 (2013) 40-61.
141. T. Silva, L. Zhao, T. Cupertino, Handwritten data clustering using agents competition in networks, J. Math. Imag. Vis. (2012) 1-13.
142. B.W. Silverman, M.C. Jones, E. Fix, J.L. Hodges, An important contribution to nonparametric discriminant analysis and density estimation: commentary on Fix and Hodges (1951), Int. Stat. Rev. 57 (December (3)) (1989) 233-238.
143. R. Smolan, J. Erwitt, The Human Face of Big Data, Against all odds productions, Sausalito, CA, 2012.
144. J. Sánchez, F. Perronnin, T. de Campos, Modeling the spatial layout of images beyond spatial pyramids, Pattern Recognit. Lett. 33 (16) (2012) 2216-2223.
145. G. Stegmayer, D. Milone, L. Kamenetzky, M. Lopez, F. Carrari, Neural network model for integration and visualization of introgressed genome and metabolite data, in: International Joint Conference on Neural Networks, IJCNN 2009, 2009, pp. 2983-2989.
146. D. Stelle, M.C. Barioni, L.P. Scott, Using data mining to identify structural rules in proteins, Appl. Math. Comput. 218 (5) (2011) 1997-2004.
147. E. Tapia, P. Bulacio, L. Angelone, Sparse and stable gene selection with consensus SVM-RFE, Pattern Recognit. Lett. 33 (2) (2012) 164-172.
148. E.S. Tellez, E. Chavez, G. Navarro, Succinct nearest neighbor search, Inf. Syst. 38 (7) (2013) 1019-1030.
149. M. Tepper, P. Musé, A. Almansa, M. Mejail, Automatically finding clusters in normalized cuts, Pattern Recognit. 44 (7) (2011) 1372-1386.
150. E. Valle, M. Cord, S. Philipp-Foliguet, D. Gorisse, Indexing personal image collections: a flexible scalable solution, IEEE Trans. Consum. Electron. 56 (August (3)) (2010) 1167-1175.
151. D. Vega, D. Rodríguez, I. Rivera, A. Suárez, Integration of association rules and clustering models obtained from multiple data sources [integración de modelos de agrupamiento y reglas de asociación obtenidos de múltiples fuentes de datos], Comput. Sist. 16 (2) (2012) 175-189.
152. S. Velasco-Forero, V. Manian, Improving hyperspectral image classification based on graphs using spatial preprocessing, in: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, vol. 3, 2008, pp. III-656-III-659.
153. M. Vieira, A. Formaggio, C. Rennó, C. Atzberger, D. Aguiar, M. Mello, Object based image analysis and data mining applied to a remotely sensed landsat time-series to map sugarcane over large areas, Remote Sens. Environ. 123 (2012) 553-562.
154. L. Vignolo, D. Milone, C. Behaine, J. Scharcanski, An evolutionary wrapper for feature selection in face recognition applications, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012, pp. 1286-1290.
155. L.D. Vignolo, D.H. Milone, J. Scharcanski, Feature selection for face recognition based on multi-objective evolutionary wrappers, Expert Syst. Appl. 40 (13) (2013) 5077-5084.
156. D. Whitley, A genetic algorithm tutorial, Stat. Comput. 4 (2) (1994) 65-85.
157. I. Wilford-Rivera, D. Ruiz-Fernández, A. Rosete-Suárez, O. Marín-Alonso, Integrating data mining models from distributed data sources, Adv. Intell. Soft Comput. 79 (2010) 389-396.
158. F.-Y. Wu, The Potts model, Rev. Mod. Phys. 54 (1) (1982) 235.
159. X. Yan, J. Han, gspan: graph-based substructure pattern mining, in: Proceedings of IEEE International Conference on Data Mining, ICDM 2003, IEEE, 2002, pp. 721-724.
160. D.C. Zanotta, V. Haertel, Gradual land cover change detection based on multitemporal fraction images, Pattern Recognit. 45 (8) (2012) 2927-2937.
161. J.A.M. Zegarra, N.J. Leite, R. da Silva Torres, Wavelet-based fingerprint image retrieval, J. Comput. Appl. Math. 227 (2) (2009) 294-307.