



@article {2283,
	title = {Comparison of a computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians.},
	journal = {Journal of perinatal medicineJ Perinat Med},
	volume = {38},
	year = {2010},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1515/JPM.2010.030PubMed ID: 20121542Language of Original Document: EnglishCorrespondence Address: Costa, M.A.email: cosantonia@gmail.com},
	month = {2010///},
	pages = {191 - 195},
	abstract = {AIMS: To compare between computer analysis of intrapartum cardiotocography (CTG) features by the Omniview-SisPorto 3.5 and a consensus of clinicians. METHODS: Agreement study using 50 consecutively acquired tracings (206 h of signals) with >60 min duration, <10\% signal loss and recorded in labor at term by internal fetal heart rate (FHR) monitoring. Tracings were divided into 10-min segments and independently analyzed by three experienced clinicians, in order to estimate the FHR baseline and identify periodic events. A consensus was reached using a three round Delphi procedure. Results were compared with the analysis provided by the Omniview-SisPorto 3.5 system. RESULTS: For baseline estimation, agreement between the computer and the consensus was high [intraclass correlation coefficient (ICC)=0.85; 95\% confidence interval (CI) 0.46-0.93], with a mean difference of 3.7 bpm (limits of agreement -4.4-11.9 bpm), and 99\% of differences under 15 bpm. A concordant identification was observed in 71\% of accelerations (95\% CI: 69\%-73\%), 68\% of decelerations (95\% CI: 66\%-70\%), and 87\% of uterine contractions (95\% CI: 85\%-89\%). CONCLUSIONS: A high agreement was observed between the Omniview-SisPorto 3.5 and a consensus of clinicians in evaluation of intrapartum CTG baseline, accelerations, decelerations and uterine contractions.},
	keywords = {algorithm, Algorithms, article, Cardiotocography, comparative study, computer assisted diagnosis, Diagnosis, Computer-Assisted, Female, Fetal Heart, Fetal monitoring, fetus heart, fetus monitoring, human, Humans, instrumentation, methodology, physician, Physicians, physiology, Pregnancy},
	isbn = {16193997 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953038696\&partnerID=40\&md5=bca187de4ffea0b2f2c4d2dcfb2140d9},
	author = {Costa, M.A. and Ayres-de-Campos, D. and Machado, A.P. and Santos, C.C. and Bernardes, J.}
}
@article {2253,
	title = {The MEE principle in data classification: A perceptron-based analysis},
	journal = {Neural ComputationNeural Comp.},
	volume = {22},
	year = {2010},
	note = {Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1162/NECO_a_00013PubMed ID: 20569178Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Porto 4200-465, Portugal; email: lmsilva@fe.up.ptReferences: Alexandre, L.A., Marques de S{\'a}, J., Error entropy minimization for LSTM training (2006) Proceedings of the 16th International Conference on Artificial Neural Networks, pp. 244-253. , Berlin: Springer;Asuncion, A., Newman, D., (2007) UCI machine learning repository. Irvine University of California, Irvine, , http://www.ics.uci.edu/~mlearn/MLRepository.html, School of Information and Computer Sciences. Available online at; 
Bishop, C., (1995) Neural networks for pattern recognition, , New York: OxfordUniversity Press; 
Erdogmus, D., Hild K., II., Pr{\'\i}ncipe, J.C., Blind source separation using R{\'e}nyi{\textquoteright}s α-marginal entropies (2002) Neurocomputing, 49, pp. 25-38; 
Erdogmus, D., Pr{\'\i}ncipe, J.C., Comparison of entropy and mean square error criteria in adaptive system training using higher order statistics (2000) Proceedings of the Intl. Conf. on ICA and Signal Separation, pp. 75-80. , Berlin: Springer-Verlag; 
Erdogmus, D., Pr{\'\i}ncipe, 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; 
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Santos, J., (2007) Data classification with neural networks and entropic criteria, , Unpublished doctoral dissertation, University of Porto; 
Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., Batch-sequential algorithm for neural networks trained with entropic criteria (2005) Proceedings of the International Conference on Artificial Neural Networks, pp. 91-96. , Berlin: Springer-Verlag; 
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Silva, L.M., Embrechts, M., Santos, J.M., Marques de S{\'a}, J., The influence of the risk functional in data classification with MLPs (2008) Proceedings of the International Conference on Artificial Neural Networks, pp. 185-194. , Berlin: Springer-Verlag; 
Silva, L.M., Felgueiras, C., Alexandre, L.A., Marques de S{\'a}, J., Error entropy in classification problems: A univariate data analysis (2006) Neural Computation, 18 (9), pp. 2036-2061; 
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},
	month = {2010///},
	pages = {2698 - 2728},
	abstract = {This letter focuses on the issue of whether risk functionals derived from information-theoretic principles, such as Shannon or R{\'e}nyi{\textquoteright}s entropies, are able to cope with the data classification problem in both the sense of attaining the risk functional minimum and implying the minimum probability of error allowed by the family of functions implemented by the classifier, here denoted by min Pe. The analysis of this so-called minimization of error entropy (MEE) principle is carried out in a single perceptron with continuous activation functions, yielding continuous error distributions. In spite of the fact that the analysis is restricted to single perceptrons, it reveals a large spectrum of behaviors that MEE can be expected to exhibit in both theory and practice. In what concerns the theoretical MEE, our study clarifies the role of the parameters controlling the perceptron activation function (of the squashing type) in often reaching the minimum probability of error. Our study also clarifies the role of the kernel density estimator of the error density in achieving the minimum probability of error in practice. {\textcopyright} 2010 Massachusetts Institute of Technology.},
	keywords = {algorithm, Algorithms, artificial intelligence, artificial neural network, automated pattern recognition, Computer simulation, entropy, letter, Mathematical Concepts, mathematical phenomena, Models, Statistical, Neural Networks (Computer), Pattern Recognition, Automated, standard, statistical model},
	isbn = {08997667 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78149334887\&partnerID=40\&md5=97c7445cfa4693d406d979afc4b42319},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@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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},
	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.}
}
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	year = {2008},
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},
	month = {2008///},
	pages = {62 - 75},
	abstract = {Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones. {\textcopyright} 2007 IEEE.},
	keywords = {algorithm, Algorithms, article, artificial intelligence, automated pattern recognition, Cluster Analysis, Clustering, Clustering algorithms, computer assisted diagnosis, Distance measurement, Entropic subgraphs, entropy, Graph theory, Graphs, Hierarchical clustering, Hierarchical systems, Image enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, methodology, Pattern Recognition, Automated, reproducibility, Reproducibility of Results, sensitivity and specificity, three dimensional imaging},
	isbn = {01628828 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-36448984894\&partnerID=40\&md5=f549049e4ae5bff1f3a95aaae22cefb8},
	author = {Santos, J.M. and De S{\'a}, J.M. and Alexandre, L.A.}
}
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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.}
}
