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J. L., C. J. Mantas, J. M. Benitez, Interpretation of Artificial Neural Networks by Means of Fuzzy Rules, IEEE Transactions on Neural Networks, Vol. 13, No. 1, 2002, pp. 101–116.

Cechin, A. L., E. Battistella, The Interpretation of Feedforward Neural Networks for Secondary Structure Prediction Using Sugeno Fuzzy Rules, International Journal of Hybrid Intelligent Systems, Vol. 4, No. 1, 2007, pp. 3–16.

Cherkassky, V., F. Mulier, Learning from Data: Concepts, Theory and Methods, John Wiley & Sons, Inc., New York, 1998.

Cios, K. J., W. Pedrycz, R. W. Swiniarski, L. A. Kurgan, Data Mining: A Knowledge Discovery Approach, Springer, New York, 2007.

Dreyfus, G., Neural Networks: Methodology and Applications, Springer, Berlin, 2005.

Embrechts, M. J., Neural Network for Data Mining, in Intelligent Engineering Systems through Artificial Neural Networks, P. Chen, B. R. Fernandez, J. Gosh, eds., ASME Press, New York, 1995, pp. 771–778.

Engel, A., C. Van den Broeck, Statistical Mechanics of Learning, Cambridge University Press, Cambridge, UK, 2001.

Fayyad, U. M., G. Piatetsky-Shapiro, P. Smith, R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Cambridge, 1996.

Finn, P., S. Muggleton, D. Page, A. Srinivasan, Pharmacophore Discovery Using the Inductive Logic Programming System Prolog, Machine Learning, Special Issue on Applications and Knowledge Discovery, Vol. 33, No. 1, 1998, pp. 13–47.

Fu, L., Neural Networks in Computer Intelligence, Mc Graw-Hill Inc., New York, 1994.

Fu, L., An Expert Network for DNA Sequence Analysis, IEEE Intelligent Systems, January/February 1999, pp. 65–71.

Hagan, M. T., H. B. Demuth, M. Beale, Neural Network Design, PWS Publishing Co., Boston, 1996.

Hand, D., H. Mannila, P. Smyth, Principles of Data Mining, The MIT Press, Cambridge, MA, 2001.

Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999.

Haykin, S., Neural Networks and Learning Machines, 3rd edition, Pearson Education Co., Upper Saddle River, NJ, 2009.

Heaton, J., Introduction to Neural Networks with Java, Heaton Research, Chesterfield, MD, 2005.

Holena, M., Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data, in Adaptive and Natural Computing Algorithms, B. Ribeiro, ed., Part IV, Springer, Secaucus, NJ, 2005, pp. 369–372.

Integral Solutions, 1999, Clementine, http://www.isl.co.uk/clem.html.

Jang, J. R., C. Sun, Neuro-Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83, No. 3, 1995, pp. 378–406.

Jang, J.-S. R., C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Inc., Upper Saddle River, NJ, 1997.

Jin, H., H. Shum, K. Leung, M. Wong, Expanding Self-Organizing Map for Data Visualization and Cluster Analysis, Information Sciences, Vol. 163, Nos. 1–3, 2004, pp. 157–173.

Kanevski, M., Classification of Interest Rate Curves Using Self-Organizing Maps, February 2008, http://arxiv.org/PS_cache/arxiv/pdf/0709/0709.4401v1.pdf.

Kanevski, M., Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy, EPFL Press, Lausanne, 2008.

Kantardzic, M., A. A. Aly, A. S. Elmaghraby, Visualization of Neural-Network Gaps Based on Error Analysis, IEEE Transactions on Neural Networks, Vol. 10, No. 2, 1999, pp. 419–426.

Kaudel, A., M. Last, H. Bunke, eds., Data Mining and Computational Intelligence, Physica-Verlag, Heidelberg, Germany, 2001.

King, R. D., et al., Is It Better to Combine Predictions? Protein Engineering, Vol. 13, No. 1, 2000, pp. 15–19.

Kukar, M., Quality Assessment of Individual Classifications in Machine Learning and Data Mining, Knowledge and Information Systems, Vol. 9, No. 3, 2006, pp. 364–384.

Munakata, T., Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm, Springer, New York, 1998.

Pal, S. K., S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, Inc., New York, 1999.

Petlenkov, A., et al., Application of Self-Organizing

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