



@conference {2200,
	title = {Proactive engineering},
	booktitle = {IEEE Global Engineering Education Conference, EDUCONIEEE Global Eng. Edu. Conf., EDUCON},
	series = {2012 IEEE Global Engineering Education Conference, EDUCON 2012},
	year = {2012},
	note = {Conference code: 91215Export Date: 21 November 2012Source: ScopusArt. No.: 6201104doi: 10.1109/EDUCON.2012.6201104Language of Original Document: EnglishCorrespondence Address: Duarte, C.; INESC TEC (Formerly INESC Porto), Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 378, 4200-465 Porto, PortugalReferences: Gheorghe, C., Covrig, M., Uncuta, S., Popescu, M., Does the implementation of the Bologna process in higher technical education require a new approach to motivate the actors involved in the educational process? (2011) 7th International Symposium on Advanced Topics in Electrical Engineering (ATEE{\textquoteright}2011), pp. 1-4. , May;(2011) Microelectronics Students{\textquoteright} Group, , http://usgroup.eu; 
(2011) BioStar, , http://biostar.fe.up.pt; 
Coke, P., Duarte, C., Cardoso, A., Tavares, V.G., De Oliveira, P.G., Network Infrastructure for academic IC CAD environments (2011) IEEE International Conference on Computer As A Tool (EUROCON{\textquoteright}2011), pp. 1-4. , Apr; 
Clarke, P., Students win training for being Cadence{\textquoteright}s fastest{\textquoteright} designers (2009) EE Times, , http://eetimes.eu, ID 217500055 May 14; 
Magalh{\~a}es, F., Oliveira, H.P., Campilho, A., (2010) SPD2010-Fingerprint Singular Points Detection Competition, , http://fe.up.pt/-spd2010/; 
Magalh{\~a}es, F., Oliveira, H.P., Matos, H., Campilho, A., (2011) HGC2011-Hand Geometric Points Detection Competition, , http://www.fe.up.pt/-hgc2011/; 
Duarte, C., Student group works on designs for a fully integrated wireless receiver (2009) IEEE Region 8 News, 2 (4), p. 9. , Dec; 
Dias, D., (2011) Computational Vision Applied to the Segmentation and Morphmetric Characterization of the Sciatic Nerve in Microscopic Images, , M.Sc. Thesis, Faculdade de Engenharia, Universidade do Porto, Jul; 
Martins, D., (2011) Biometric Recognition Based on the Texture Along Palmprint Principal Lines, , M.Sc. Thesis, Faculdade de Engenharia, Universidade do Porto, Jul; 
Matos, H., (2011) Hand Geometry Based Recognition System, , M.Sc. Thesis, Faculdade de Engenharia, Universidade do Porto, Jan; 
Soares, J., (2011) Uncalibrated Stereo Vision Applied to Breast Cancer Treatment Aesthetic Assessment, , M.Sc. Thesis, Faculdade de Engenharia, Universidade do Porto, Jul; 
Matos, H., Magalh{\~a}es, F., Oliveira, H.P., Cardoso, J.S., Hand geometry based recognition system-A new method with no constraints on image acquisition (2011) Proceedings of the 4th Meeting of Young Researchers of the University of Porto; 
Gaspar, D., Bre, L., Bishoff, N., Domingues, I., Mammograms image processing (2011) Proceedings of the 4th Meeting of Young Researchers of the University of Porto; 
Magalh{\~a}es, F., Oliveira, H.P., Campilho, A.C., A new method for the detection of singular points in fingerprint images (2009) Proceedings of the IEEE Workshop on Applications of Computer Vision, pp. 157-162; 
Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S., INbreast: Toward a full-field digital mammographic database (2011) Academic Radiology; 
Oliveira, H.P., Magalhaes, A., Cardoso, M.J., Cardoso, J.S., An accurate and interpretable model for BCCT.core (2010) Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6158-6161; 
(2011) IJUP-Junior Reasearch at UP, , http://ijup.up.pt, University of Porto
},
	month = {2012///},
	address = {Marrakech},
	abstract = {This paper presents two initiatives run by groups of engineering students at the University of Porto: the Microelectronics Students{\textquoteright} Group and BioStar. These groups are student-led initiatives that promote different scientific fields through self-guided projects. Both experiences have proven to be very successful in increasing the undergraduate student{\textquoteright}s interest in science and technology. This work reports the activities, organization and main methodologies employed by these groups, which can be seen as successful approaches to enhance the technical curriculum of students. {\textcopyright} 2012 IEEE.},
	keywords = {Curricula, Engineering education, Microelectronics, Project based learning, project-based learning, Science and Technology, Scientific fields, student-led initiatives, Students, Undergraduate students},
	isbn = {21659559 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864143084\&partnerID=40\&md5=3b616738c3bc4fbca798784210235f95},
	author = {Duarte, C. and Oliveira, H.P. and Magalhaes, F. and Tavares, V.G. and Campilho, A.C. and De Oliveira, P.G.}
}
@conference {2226,
	title = {Automatic segmentation of chromatographic images for region of interest delineation},
	booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIEProgr. Biomed. Opt. Imaging Proc. SPIE},
	series = {Medical Imaging 2011: Image Processing},
	volume = {7962},
	year = {2011},
	note = {Conference code: 84913Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusArt. No.: 79623Bdoi: 10.1117/12.877671Language of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A. M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Zarate, Y., Hopkin, R., Fabry{\textquoteright}s disease (2008) The Lancet, 372 (9647), pp. 1427-1435;Gerasimov, A.V., Use of the Software Processing of Scanned Chromatogram Images in Quantitative Planar Chromatography (2004) J. of Anal Chem., 59 (4), pp. 348-353; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Comput Methods Programs Biomed, 77 (3), pp. 209-231; 
Sousa, A.V., Mendonca, A.M., Campilho, A., Chromatographic pattern classification (2008) IEEE Trans on Biomedical Engineering, 55 (6), pp. 1687-1696; 
Bajla, I., Rubl{\'\i}k, F., Arendack{\'a}, B., Farka{\v s}, I., Horni{\v s}ov{\'a}, K., {\v S}tolc, S., Witkovsk{\'y}, V., Segmentation and supervised classification of image objects in Epo doping-control (2009) Machine Vision and Applications, 20 (4), pp. 243-259; 
Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of Thin-Layer Chromatography (2004) LNCS, 3212, pp. 158-216. , Springer; 
Dempster, A., Laird, N., Rubin, D., Maximum Likelihood from incomplete data via the EM algorithm (1977) J. of the Royal Statistical Society. Series B, 39 (1), pp. 1-38; 
Heijden, F., Duin, R.P.W., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation - An Engineering Approach Using Matlab, , John Wiley \& Sons
Sponsors: The Society of Photo-Optical Instrumentation Engineers (SPIE); Dynasil Corporation/RMD Research; American Association of Physicists in Medicine (AAPM); DQE Instruments, Inc.; Ocean Thin Films, Inc.},
	month = {2011///},
	address = {Lake Buena Vista, FL},
	abstract = {This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic images, thus allowing the definition of the image area that contains the fundamental information for further processing while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully evaluated in a dataset of 41 chromatographic images. {\textcopyright} 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).},
	keywords = {Automatic analysis, Automatic segmentations, Chromatographic images, Chromatographic plates, Chromatography, Data sets, Distance feature, Fabry disease, Image pixels, Image segmentation, Imaging systems, Medical imaging, Morphological operator, Pixels, Rectangular area, Region of interest, Region of interest delineation, Screening tool, Second phase, Segmentation methods, Segmentation results, Unsupervised learning, Unsupervised learning method},
	isbn = {16057422 (ISSN); 9780819485045 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79958001297\&partnerID=40\&md5=e679e7314308e03c9042ba3909310a7c},
	author = {Mendon{\c c}a, A.M. and Sousa, A.V. and S{\'a}-Miranda, M.C. and Campilho, A.C.}
}
@conference {2292,
	title = {A new method for the detection of singular points in fingerprint images},
	booktitle = {2009 Workshop on Applications of Computer Vision, WACV 2009Workshop Appl. Comput. Vis., WACV},
	series = {2009 Workshop on Applications of Computer Vision, WACV 2009},
	year = {2009},
	note = {Conference code: 79821Export Date: 21 November 2012Source: ScopusArt. No.: 5403106doi: 10.1109/WACV.2009.5403106Language of Original Document: EnglishCorrespondence Address: Magalh{\~a}es, F.; INESC, Porto, Portugal; email: filipe.magalhaes@inescporto.ptReferences: Drets, G.A., Liljenstr{\"o}m, H.G., (1999) Intelligent Biometric Techniques in Fingerprint and Face Recognition, , CRC Press, New York;Hong, L., Wan, Y., Fingerprint image enhancement: Algorithm and performance evaluation (1998) IEEE Transactions On, Pattern Analysis and Machine Intelligence, 20 (8), pp. 777-789; 
Jain, A., Hong, L., Bolle, R., On-line fingerprint verification (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (4), pp. 302-314; 
Jinwei, G., Jie, Z., A novel model for orientation field of fingerprints (2003) Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, pp. II493-II498; 
Kara, K., Jain, A.K., Fingerprint classification (1996) Pattern Recognition, 29 (3), pp. 389-404; 
Kass, M., Witkin, A., Analyzing oriented patterns (1987) Cornput. Vision Graph. Image Process, 37 (3), pp. 362-385. , 27996; 
Kawagoe, M., Tojo, A., Fingerprint pattern classification (1984) Pattern Recognition, 17 (3), pp. 295-303; 
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K., FVC2002: Second fingerprint verification competition (2002) Proceedings - International Conference on Pattern Recognition, 16 (3), pp. 811-814; 
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K., Fvc2004: Third fingerprint verification competition (2004) Proceedings International Conference on Biometric Authentication (ICBA), pp. 1-7. , Hong Kong; 
Pankanti, S., Prabhakar, S., Jain, A.K., On the individuality of fingerprints (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (8), pp. 1010-1025; 
Sherlock, B.G., Monro, D.M., A model for interpreting fingerprint topology (1993) Pattern Recognition, 26 (7), pp. 1047-1055; 
Srinivasan, V.S., Murthy, N.N., Detection of singular points in fingerprint images (1992) Pattern Recognition, 25 (2), pp. 139-153; 
Zhou, J., Chen, F., Gu, J., A novel algorithm for detecting singular points from fingerprint images (2009) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (7), pp. 1239-1250
},
	month = {2009///},
	address = {Snowbird, UT},
	abstract = {Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincar{\'e} Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5\% to 7\%. {\textcopyright} 2009 IEEE.},
	keywords = {Algorithms, Biometric identifications, Biometrics, Computer applications, Computer vision, Critical steps, Fingerprint analysis, Fingerprint images, Forensic applications, Human intervention, Identification method, Orientation fields, Poincare, Ridge patterns, Singular points, Structural optimization, Technical presentations, Topological structure},
	isbn = {9781424454976 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77951158127\&partnerID=40\&md5=718a6649dbc13fcb895f23ffff1363d4},
	author = {Magalh{\~a}es, F. and Oliveira, H.P. and Campilho, A.C.}
}
@conference {2446,
	title = {Spectral methods in image segmentation: A combined approach},
	booktitle = {Lecture Notes in Computer ScienceLect. Notes Comput. Sci.},
	series = {Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005},
	volume = {3523},
	year = {2005},
	note = {Conference code: 65649Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Monteiro, F.C.; INEB - Instituto de Engenharia Biom{\'e}dica, Campus de Santa Apol{\'o}nia, Apartado 134, 5301-857 Bragan{\c c}a, Portugal; email: monteiro@ipb.ptReferences: Fowlkes, C., Belongie, S., Chung, F., Malik, J., Spectral grouping using nystr{\"o}m method (2004) Trans. on Pattern Analysis and Machine Intelligence, 26 (2), pp. 214-225;Malik, J., Belongie, S., Leung, T., Shi, T., Contour and texture analysis for image segmentation (2001) International Journal of Computer Vision, 43 (1), pp. 7-27; 
Martin, D., Fowlkes, C., Tal, D., Malik, J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics (2001) IEEE Int. Conf. on Computer Vision, 2, pp. 416-424; 
Meila, M., Shi, J., Learning segmentation by random walks (2000) Neural Information Processing Systems, pp. 873-879; 
Ng, A., Jordan, M., Weiss, Y., On spectral clustering: Analysis and an algorithm (2002) Advances in Neural Information Processing Systems, 14, pp. 849-856; 
Scott, D.S., Solving sparse symmetric generalized eigenvalue without factorization (1981) SIAM Journal of Numerical Analisys, 18, pp. 102-110. , http://www.netlib.org/laso/snlaso.f; 
Scott, G.L., Longuet-Higgins, H.C., Feature grouping by "relocalisation" of eigenvectors of the proximity matrix (1990) Proc. British Machine Vision Conf., pp. 103-108; 
Shi, J., Malik, J., Normalized cuts and image segmentation (2000) IEEE Trans. on Pattern Analysis and Machine Intelligence, 22 (8), pp. 888-905; 
Weiss, Y., Segmentation using eigenvectors: A unifying view (1999) International Conference on Computer Vision, pp. 975-982; 
Wertheimer, M., Laws of organization in perceptual forms (1938) A Sourcebook of Gestalt Psychology, pp. 71-88. , W. B. Ellis Editor; 
Wu, Z., Leahy, R., An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation (1993) IEEE Trans. on Pattern Analysis and Machine Intelligence, 15, pp. 1101-1113; 
Yu, S., Shi, J., Multiclass spectral clustering (2003) International Conference on Computer Vision, pp. 313-319. , Nice, France
},
	month = {2005///},
	pages = {191 - 198},
	address = {Estoril},
	abstract = {Grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated a good performance on this task using spectral methods that are based on the eigensolution of a similarity matrix. In this paper, we implement a variation of the existing methods that combines aspects from several of the best-known eigenvector segmentation algorithms to produce a discrete optimal solution of the relaxed continuous eigensolution. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Algorithms, Computer vision, Discrete optimal solution, Eigensolution, Eigenvalues and eigenfunctions, Image segmentation, Matrix algebra, Problem solving, Similarity matrix, Spectral methods},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-25144437374\&partnerID=40\&md5=5b6351af4ac58a16c41b5558a48ae9e2},
	author = {Monteiro, F.C. and Campilho, A.C.},
	editor = {Marques J.S. and Perez de la Blanca N. and Pina P.}
}
@conference {2525,
	title = {Real-time implementation of an optical flow algorithm},
	booktitle = {Proceedings - International Conference on Pattern RecognitionProc. Int. Conf. Pattern Recognit.},
	volume = {16},
	year = {2002},
	note = {Cited By (since 1996): 29Export Date: 21 November 2012Source: ScopusCODEN: PICRELanguage of Original Document: EnglishCorrespondence Address: Correia, M.V.; Instituto de Engenharia Biom{\'e}dica, Faculdade de Engenharia, Dept. Eng. Electrot{\'e}cnica e de Computadores, R. Dr. Roberto Frias, 4200-465 Porto, Portugal; email: mcorreia@fe.up.ptReferences: Barron, J.L., Fleet, D.J., Beauchemin, S.S., Performance of optical flow techniques (1994) International Journal of Computer Vision, 12 (1), pp. 43-77;Correia, M.V., Campilho, A.C., Santos, J.A., Nunes, L.B., Optical flow techniques applied to the calibration of visual perception experiments (1996) Proceedings of 13th International Conference on Pattern Recognition, ICPR {\textquoteright}96, pp. 498-502. , Vienna, Austria, August; 
Fleet, D.J., Langley, K., Recursive filters for optical flow (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (1), pp. 61-67. , January; 
Fleury, M., Clark, A.F., Downton, A.C., Evaluating optical-flow algorithms on a parallel machine (2001) Image and Vision Computing, 19, pp. 131-143; 
Lin, T., Barron, J.L., Image reconstruction error for optical flow (1994) Proceedings of Vision Interface{\textquoteright}94, pp. 73-80. , Banff National Park, Canada, May; 
Liu, H., Hong, T.-H., Herman, M., Camus, T., Accuracy vs efficiency trade-offs in optical flow algorithms (1998) Computer Vision and Image Understanding, 72 (3), pp. 271-286. , December; 
Lucas, B.D., (1984) Generalized Image Matching by the Method of Differences, , PhD thesis, Carnegie Mellon University; 
Olson, T.J., Taylor, J.R., Lockwood, R.J., Programming a pipelined image processor (1996) Computer Vision and Image Understanding, 64 (3), pp. 351-367. , November; 
Santos, J., Noriega, P., Correia, M., Campilho, A., Albuquerque, P., (2000) Object Motion Segmentation and Optical Flow Contrast, , Relat{\'o}rios de investiga{\c c}{\~a}o C.E.E.P., Universidade do Minho, Braga; 
Simoncelli, E.P., (1993) Distributed Representation and Analysis of Visual Motion, , PhD thesis, Massachusetts Institute of Technology, January
},
	month = {2002///},
	pages = {247 - 250},
	abstract = {This paper presents the implementation of an optical flow algorithm on a pipeline image processor. The overall optical flow computation method is presented and evaluated on a common set of image sequences. Results are compared to other implementations according to two different error measures. Due to its deterministic architecture, this implementation achieves very low computation delays that allow it to operate at standard video frame-rate and resolutions. It compares favorably to recent implementations in parallel hardware. {\textcopyright} 2002 IEEE.},
	keywords = {Algorithms, Computational methods, Image analysis, Image processors, Image sequences, Optical flow algorithm, Optical flows, Real time systems, Video frame-rate, Video signal processing},
	isbn = {10514651 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645769017\&partnerID=40\&md5=3ee6601ed756c942925be919a4e34178},
	author = {Correia, M.V. and Campilho, A.C.}
}
@article {2542,
	title = {On combining classifiers using sum and product rules},
	journal = {Pattern Recognition LettersPattern Recogn. Lett.},
	volume = {22},
	year = {2001},
	note = {Cited By (since 1996): 51Export Date: 21 November 2012Source: Scopusdoi: 10.1016/S0167-8655(01)00073-3Language of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; INEB-Inst. de Engenharia Biomedica, Rua Dr. Roberto Frias s/n, Porto 4200-465, Portugal; email: lfbaa@noe.ubi.ptReferences: Alexandre, L., Campilho, A., Kamel, M., Combining independent and unbiased classifiers using weighted average (2000) Proc. 15th Internat. Conf. on Pattern Recognition, 2. , IEEE Press, Barcelona, Spain;Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford University Press, Oxford; 
Blake, C., Keogh, E., Merz, C., (1998) UCI Repository of Machine Learning Databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
Demuth, H., Beale, M., (1998) Neural Network Toolbox User{\textquoteright}s Guide, , The MathWorks, Inc; 
Hansen, L., Salamon, P., Neural network ensembles (1990) IEEE Trans. PAMI, 12 (10), pp. 993-1001; 
Hashem, S., Optimal linear combinations of neural networks (1997) Neural Networks, 10 (4), pp. 599-614; 
Kittler, J., Hatef, M., Duin, R., Combining classifiers (1996) Proc. Internat. Conf. on Pattern Recognition{\textquoteright}96; 
Kittler, J., Hatef, M., Duin, R., Matas, J., On combining classifiers (1998) IEEE Trans. PAMI, 20 (3), pp. 226-239; 
Opitz, D., Maclin, R., Popular ensemble methods: An empirical study (1999) J. Artif. Intell. Res, 11, pp. 169-198; 
Richard, M., Lippmann, R., Neural network classifiers estimate Bayesian a posteriori probabilities (1991) Neural Comput, 3, pp. 461-483; 
Rogova, G., Combining the results of several neural network classifiers (1994) Neural Networks, 7 (5), pp. 777-781; 
Tax, D., Duin, R., Breukelen, M., Comparison between product and mean classifier combination rules (1997) Workshop on Statistical Techniques in Pattern Recognition, , Prague Czech Republic; 
Ueda, N., Optimal linear combination of neural networks for improving classification performance (2000) IEEE Trans. PAMI, 22 (2), pp. 207-215
},
	month = {2001///},
	pages = {1283 - 1289},
	abstract = {This paper presents a comparative study of the performance of arithmetic and geometric means as rules to combine multiple classifiers. For problems with two classes, we prove that these combination rules are equivalent when using two classifiers and the sum of the estimates of the a posteriori probabilities is equal to one. We also prove that the case of a two class problem and a combination of two classifiers is the only one where such equivalence occurs. We present experiments illustrating the equivalence of the rules under the above mentioned assumptions. {\textcopyright} 2001 Elsevier Science B.V. All rights reserved.},
	keywords = {artificial neural network, Classification, Classifier fusion, Combinatorial mathematics, Combining classifiers, Equivalence classes, Estimation, K nearest-neighbours, Multiple classifiers, Neural networks, Pattern recognition, probability},
	isbn = {01678655 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0034875843\&partnerID=40\&md5=becfe9c0c4a7d97f7bbaf33f3059ef8f},
	author = {Alexandre, L.A. and Campilho, A.C. and Kamel, M.}
}
