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[603] JE Moody The E ective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems In J Moody, SJ Hanson, and R Lippmann, editors, Advances in Neural Information Processing Systems, volume 4, pages 847 854, 1992 [604] JE Moody Prediction Risk and Architecture Selection for Neural Networks In V Cherkassky, JH Friedman, and H Wechsler, editors, From Statistics to Neural Networks: Theory and Pattern Recognition Applications, pages 147 165 Springer, 1994 [605] JE Moody and C Darken Learning with Localized Receptive Fields In D Touretzky, G Hinton, and T Sejnowski, editors, Proceedings of the Connectionist Models Summer School, pages 133 143, San Mateo, CA, 1989 Morgan Kaufmann [606] K Mori, M Tsukiyama, and T Fukada Immune Algorithm with Searching Diversity and Its Application to Resource Allocation Problems Transactions of the Institute of Electrical Engineers of Japan, 113(10):872 878, 1993 [607] N Mori, S Imanishi, H Kita, and Y Nishikawa Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm In Proceedings of the Seventh International Conference on Genetic Algorithms, pages 299 306, 1997 [608] P Morillo, M Fern ndez, and JM Ordu a An ACS-Based Partitioning Method a n for Distributed Virtual Environment Systems In Proceedings of the International Parallel and Distributed Processing Symposium, page 148, 2003 [609] S Mostaghim and J Teich Strategies for Finding Local Guides in MultiObjective Particle Swarm Optimization (MOPSO) In Proceedings of the IEEE Swarm Intelligence Symposium, pages 26 33, 2003 [610] S Mostaghim and J Teich The Role of -dominance in Multi-objective Particle Swarm Optimization Methods In Proceedings of the IEEE Congress on Evolutionary Computation, pages 1764 1771, 2003 [611] MC Mozer and P Smolensky Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment In DS Touretzky, editor, Advances in Neural Information Processing Systems, volume 1, pages 107 115, 1989 [612] K-R M ller, M Finke, N Murata, K Schulten, and S Amari A Numerical u Study on Learning Curves in Stochastic Multi-Layer Feed-Forward Networks Neural Computation, 8(5):1085 1106, 1995 [613] SD M ller, IF Sbalzarini, JH Walther, and PD Koumoutsakos Evolution u Strategies for the Optimization of Microdevices In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, pages 302 309, 2001 [614] SD M ller, NN Schraudolph, and PD Koumoutsakos Step Size Adaptation u in Evolution Strategies using Reinforcement Learning In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, pages 151 156, 2002 [615] Y Murakami, H Sato, and A Namatame Co-evolution in Negotiation Games In Proceedings of the Fourth International Conference on Computational Intelligence and Multimedia Applications, pages 241 245, 2001
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[616] N Murata, S Yoshizawa, and S Amari A Criterion for Determining the Number of Parameters in an Arti cial Neural Network Model In T Kohonen, K M kisara, O Simula, and J Kangas, editors, Artificial Neural Networks, a pages 9 14 Elsevier Science Publishers, 1991 [617] N Murata, S Yoshizawa, and S Amari Learning Curves, Model Selection and Complexity of Neural Networks In C Lee Giles, SJ Hanson, and JD Cowan, editors, Advances in Neural Information Processing Systems, volume 5, pages 607 614, 1994 [618] N Murata, S Yoshizawa, and S Amari Network Information Criterion Determining the Number of Hidden Units for an Arti cial Neural Network Model IEEE Transactions on Neural Networks, 5(6):865 872, 1994 [619] S Naka, T Genji, T Yura, and Y Fukuyama Practical Distribution State Estimation using Hybrid Particle Swarm Optimization In IEEE Power Engineering Society Winter Meeting, volume 2, pages 815 820, 2001 [620] D Nam, YD Seo, L-J Park, CH Park, and B Kim Parameter Optimization of an On-Chip Voltage Reference Circuit using Evolutionary Programming IEEE Transactions on Evolutionary Computation, 5(4):414 421, 2001 [621] H Narihisa, T Taniguchi, M Thuda, and K Katayama E ciency of Parallel Exponential Evolutionary Programming In Proceedings of the International Conference Workshop on Parallel Processing, pages 588 595, 2005 [622] O Nasraoui, D Dasgupta, and F Gonzalez The Promise and Challenges of Arti cial Immune System Based Web Usage Mining: Preliminary Results In Proceedings of the Second SIAM International Conference on Data Mining, pages 29 39, 2002 [623] O Nasraoui, F Gonzalez, C Cardona, C Rojas, and D Dasgupta A Scalable Arti cial Immune System Model for Dynamic Unsupervised Learning In Proceedings of the Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science, volume 2723, pages 219 230 Springer-Verlag, 2003 [624] D Naug and R Gadagkar The Role of Age in Temporal Polyethism in a Primitively Eusocial Wasp Behavioral Ecology and Sociobiology, 42:37 47, 1998 [625] D Naug and R Gadagkar Flexible Division of Labor Mediated by Social Interactions in an Insect Colony A Simulation Model Journal of Theoretical Biology, 197:123 133, 1999 [626] M Neal An Arti cial Immune System for Continuous Analysis of Time-varying Data In Proceedings of the First International Conference on Artificial Immune Systems, volume 1, pages 76 85, 2002 [627] M Neethling and AP Engelbrecht Determining RNA Secondary Structure using Set-Based Particle Swarm Optimization In Proceedings of the IEEE Congress on Evolutionary Computation, pages 1670 1677, 2006 [628] L Nemes and T Roska A CNN Model of Oscillation and Chaos in Ant Colonies: A Case Study IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 42(10):741 745, 1995 [629] NJ Nilsson Artificial Intelligence: A New Synthesis Morgan Kaufmann, 1998 [630] M Niranjan and F Fallside Neural Networks and Radial Basis Functions in Classifying Static Speech Patterns Technical Report CUEDIF-INFENG17R22, Engineering Department, Cambridge University, 1988
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