



@article {2335,
	title = {Data classification with multilayer perceptrons using a generalized error function},
	journal = {Neural NetworksNeural Netw.},
	volume = {21},
	year = {2008},
	note = {Cited By (since 1996): 6Export Date: 21 November 2012Source: ScopusCODEN: NNETEdoi: 10.1016/j.neunet.2008.04.004PubMed ID: 18572384Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; INEB-Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Baum, E.B., Wilczek, F., Supervised learning of probability distributions by neural networks (1987) NIPS, pp. 52-61. , Anderson D.Z. (Ed), American Institute of Physics;Bishop, C., (1995) Neural networks for pattern recognition, , Oxford University Press; 
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M{\o}ller, M. (1993). Efficient training of feed-forward neural networks. Ph.D. thesis. Computer Science Department - Aarhus UniversityMarques de S{\'a}, J., (2001) Pattern recognition: Concepts, methods and applications, , Springer Verlag; 
Matsuoka, K., \& Yi, J. (1991). Backpropagation based on the logarithmic error function and elimination of local minima. In Proceedings of the 1990 IEEE international joint conference on neural networksRichard, M., Lippmann, R., Neural network classifiers estimate bayesian a posteriori probabilities (1991) Neural Computation, 3, pp. 461-483; 
Santos, J., Alexandre, L., \& Marques~de S{\'a}, J. (2004). The error entropy minimization algorithm for neural network classification. In int. conf. on recent advances in soft computingSilva, L., Alexandre, L., Marques de S{\'a}, J., Neural network classification: Maximizing zero-error density (2005) LNCS, 3686, pp. 127-135. , ICAPR 2005; 
Silva, L., Alexandre, L.A., \& Marques~de S{\'a}, J. (2006). New developments of the Z-EDM algorithm. In Proceedings of the sixth international conference intelligent systems design and applications: Vol.~1 (pp. 1067-1072)Silva, L., Marques~de S{\'a}, J., \& Alexandre, L. (2005). Neural network classification using Shannon{\textquoteright}s entropy. In European symposium on artificial neural networksSilverman, B., (1986) Density estimation for statistics and data analysis, , Chapman \& Hall; 
Solla, S.A., Levin, E., Fleisher, M., Accelerated learning in layered neural networks (1988) Complex Systems, 2 (6), pp. 625-639
},
	month = {2008///},
	pages = {1302 - 1310},
	abstract = {The learning process of a multilayer perceptron requires the optimization of an error function E (y, t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E                        Exp, inspired by the Z-EDM algorithm that we have recently proposed. An important property of E
                        Exp is its ability to emulate the behavior of other error functions by the sole adjustment of a real-valued parameter. In other words, E
                        Exp is a sort of generalized error function embodying complementary features of other functions. The experimental results show that the flexibility of the new, generalized, error function allows one to obtain the best results achievable with the other functions with a performance improvement in some cases. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.
},
	keywords = {algorithm, Algorithms, analytical error, article, calculation, Classification, Complementary features, Cybernetics, data analysis, Data classification, Data classifications, Data Interpretation, Statistical, Electric loads, entropy, Error functions, learning, Learning processes, mathematical analysis, Mathematical properties, Models, Statistical, Multilayer neural networks, Multilayer perceptron, Multilayer perceptrons, Multilayers, Neural networks, Neural Networks (Computer), Pattern recognition systems, perceptron, Performance improvements, priority journal, Probability density function},
	isbn = {08936080 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-54449100428\&partnerID=40\&md5=30f8de18c0272f4a440395045bd3c164},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@book {2331,
	title = {The influence of the risk functional in data classification with MLPs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5163 LNCS},
	year = {2008},
	note = {Conference code: 73798Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-87536-9_20Language of Original Document: EnglishCorrespondence Address: Silva, L. M.; INEB -Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, Portugal; email: lmsilva@fe.up.ptReferences: Silva, L.M., Alexandre, L.A., Marques de S{\'a}, J.: Data classification with multilayer perceptrons using a generalized error function. Neural Networks (2008) doi:10.1016/j.neunet.2008.04,004Erdogmus, D., Principe, J.C., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035-1044;Erdogmus, D., Principe, J.C., An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J.M., Alexandre, L.A., Marques de S{\'a}, J., The Error Entropy Minimization Algorithm for Neural Network Classification (2004) Int. Conf. on Recent Advances in Soft Computing, , Nottingham, United Kingdom; 
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Marques de S{\'a}, J., (2007) Using SPSS, STATISTICA, MATLAB and R, , 2nd edn. Springer, Heidelberg; 
Embrechts, M.J., Szymanski, B., Sternickel, M., Introduction to Scientific Data Mining: Direct Kernel Methods and Applications (2004) Computationally Intelligent Hybrid Systems, pp. 317-363. , ch. 10, pp, Wiley Interscience, Chichester; 
Bennett, K.P., Embrechts, M.J., An Optimization Perspective on Kernel Partial Least Squares Regression (2003) Models and Applications. NATO Science Series, 3, pp. 227-249. , Advances in Learning Theory: Methods, 190, pp, IOS Press, Amsterdam; 
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Sch{\"o}lkopf, B., Smola, A.J., (2002) Learning with Kernels, , MIT Press, Cambridge
},
	month = {2008///},
	pages = {185 - 194},
	address = {Prague},
	abstract = {We investigate the capability of multilayer perceptrons using specific risk functionals attaining the minimum probability of error (optimal performance) achievable by the class of mappings implemented by the multilayer perceptron (MLP). For that purpose we have carried out a large set of experiments using different risk functionals and datasets. The experiments were rigorously controlled so that any performance difference could only be attributed to the different risk functional being used. Statistical analysis was also conducted in a careful way. From the several conclusions that can be drawn from our experimental results it is worth to emphasize that a risk functional based on a specially tuned exponentially weighted distance attained the best performance in a large variety of datasets. As to the issue of attaining the minimum probability of error we also carried out classification experiments using non-MLP classifiers that implement complex mappings and are known to provide the best results until this date. These experiments have provided evidence that at least in many cases, by using an adequate risk functional, it will be possible to reach the optimal performance. {\textcopyright} Springer-Verlag Berlin Heidelberg 2008.},
	keywords = {Backpropagation, Complex mapping, Data classification, Data sets, Electric loads, Experiments, Functionals, Minimum probability of error, MLP classifiers, Multi layer perceptron, Multi-layer perceptrons, Multilayer neural networks, Multilayers, Optimal performance, Pattern recognition systems, Probability density function, risks, Statistical analysis, Weighted distance},
	isbn = {03029743 (ISSN); 3540875352 (ISBN); 9783540875352 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-58849129862\&partnerID=40\&md5=d9d0d90a540a21b191eebbf1956ed6e9},
	author = {Silva, L.M. and Embrechts, M. and Santos, J.M. and De S{\'a}, J.M.}
}
@article {2562,
	title = {Object recognition in image sequences with cellular neural networks},
	journal = {NeurocomputingNeurocomputing},
	volume = {31},
	year = {2000},
	note = {Cited By (since 1996): 14Export Date: 21 November 2012Source: ScopusCODEN: NRCGEdoi: 10.1016/S0925-2312(99)00177-0Language of Original Document: EnglishCorrespondence Address: Buker, U.; Heinz Nixdorf Institute, University of Paderborn, Department of Electrical Engineering, 33095 Paderborn, Germany; email: bueker@get.uni-paderborn.deReferences: Bradski, G., Grossberg, S., Fast-learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional views (1995) Neural Networks, 8 (7-8), pp. 1053-1080;B{\"u}ker, U., Hartmann, G., Knowledge based view control of a neural 3D object recognition system (1996) In: Proceedings of the 13th International Conference on Pattern Recognition, ICPR{\textquoteright}96, Wien, 4, pp. 24-29. , IEEE Computer Soc. Press, Los Alamitos; 
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},
	month = {2000///},
	pages = {125 - 141},
	publisher = {Elsevier Science B.V.},
	abstract = {In this paper, the application of CNN associative memories for 3D object recognition is presented. The main idea is to analyse the optical flow in an image sequence of an object. Several features of the optical flow between two succeeding images are calculated and merged to a time series of features for the whole image sequence. These features show several object specific characteristics and are used for a classification step in an object recognition system. Therefore, the feature vectors of an object set are learnt and recalled by an associative memory based on the paradigm of cellular neural networks (CNN). (C) 2000 Elsevier Science B.V.In this paper, the application of CNN associative memories for 3D object recognition is presented. The main idea is to analyze the optical flow in an image sequence of an object. Several features of the optical flow between two succeeding images are calculated and merged to a time series of features for the whole image sequence. These features show several object specific characteristics and are used for a classification step in an object recognition system. Therefore, the feature vectors of an object set are learnt and recalled by an associative memory based on the paradigm of cellular neural networks (CNN).
},
	keywords = {algorithm, artificial neural network, Associative memory, Associative storage, Cellular neural networks, Computational methods, computer system, Image analysis, Image sequences, mathematical computing, Object recognition, Optical flow, Optical flows, Pattern recognition, Pattern recognition systems, priority journal, review, Three dimensional computer graphics, Time series analysis, Vectors},
	isbn = {09252312 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0343852159\&partnerID=40\&md5=ecb858090fa38d7d39e6142515efac59},
	author = {Milanova, M. and B{\"u}ker, U.}
}
