A New Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks
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Xin Yao | Kazuyuki Murase | Md. Monirul Islam | Md. Faijul Amin | Md. Abdus Sattar | X. Yao | K. Murase | Md. Abdus Sattar | M. Islam
[1] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[2] Ron Meir,et al. Evolving a learning algorithm for the binary perceptron , 1991 .
[3] Kurt Hornik,et al. Some new results on neural network approximation , 1993, Neural Networks.
[4] John R. Koza,et al. Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[5] Oded Maimon,et al. Design architectures and training of neural networks with a distributed genetic algorithm , 1993, IEEE International Conference on Neural Networks.
[6] Mikko Lehtokangas,et al. Modified cascade-correlation learning for classification , 2000, IEEE Trans. Neural Networks Learn. Syst..
[7] Md. Monirul Islam,et al. Graph Matching Recombination for Evolving Neural Networks , 2007, ISNN.
[8] Alfred Jean Philippe Lauret,et al. A node pruning algorithm based on a Fourier amplitude sensitivity test method , 2006, IEEE Transactions on Neural Networks.
[9] Lutz Prechelt,et al. Some notes on neural learning algorithm benchmarking , 1995, Neurocomputing.
[10] Neil Burgess,et al. A Constructive Algorithm that Converges for Real-Valued Input Patterns , 1994, Int. J. Neural Syst..
[11] James T. Kwok,et al. Objective functions for training new hidden units in constructive neural networks , 1997, IEEE Trans. Neural Networks.
[12] Gustavo Deco,et al. Two Strategies to Avoid Overfitting in Feedforward Networks , 1997, Neural Networks.
[13] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[14] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[15] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[16] Darrell Whitley,et al. Optimizing small neural networks using a distributed genetic algorithm , 1990 .
[17] Lawrence J. Fogel,et al. Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming , 1999 .
[18] John Moody,et al. Prediction Risk and Architecture Selection for Neural Networks , 1994 .
[19] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[20] Hans-Paul Schwefel,et al. Numerical optimization of computer models , 1981 .
[21] Jean-Pierre Nadal,et al. Study of a Growth Algorithm for a Feedforward Network , 1989, Int. J. Neural Syst..
[22] David B. Fogel,et al. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .
[23] Kazuyuki Murase,et al. A New Crossover Operator and Its Application to Artificial Neural Networks Evolution , 2001 .
[24] James T. Kwok,et al. Constructive algorithms for structure learning in feedforward neural networks for regression problems , 1997, IEEE Trans. Neural Networks.
[25] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[26] Rudy Setiono,et al. Use of a quasi-Newton method in a feedforward neural network construction algorithm , 1995, IEEE Trans. Neural Networks.
[27] Lawrence J. Fogel,et al. Artificial Intelligence through Simulated Evolution , 1966 .
[28] Kazuyuki Murase,et al. A new algorithm to design compact two-hidden-layer artificial neural networks , 2001, Neural Networks.
[29] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[30] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[31] C. Lee Giles,et al. An analysis of noise in recurrent neural networks: convergence and generalization , 1996, IEEE Trans. Neural Networks.
[32] Xin Yao,et al. A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.
[33] Andries Petrus Engelbrecht,et al. A new pruning heuristic based on variance analysis of sensitivity information , 2001, IEEE Trans. Neural Networks.
[34] F. Heimes,et al. Traditional and evolved dynamic neural networks for aircraft simulation , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.
[35] Xin Yao,et al. A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.
[36] Roberto Togneri,et al. Modelling 1-D signals using Hermite basis functions , 1997 .
[37] Fred W. Glover,et al. Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..
[38] Dušan Petrovački,et al. Evolutional development of a multilevel neural network , 1993, Neural Networks.
[39] Timur Ash,et al. Dynamic node creation in backpropagation networks , 1989 .
[40] Xin Yao,et al. A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..
[41] J. D. Schaffer,et al. Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.
[42] Teresa Bernarda Ludermir,et al. An Optimization Methodology for Neural Network Weights and Architectures , 2006, IEEE Transactions on Neural Networks.
[43] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[44] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[45] Kazuyuki Murase,et al. A New Constructive Algorithm for Designing and Training Artificial Neural Networks , 2007, ICONIP.
[46] Harry Wechsler,et al. From Statistics to Neural Networks: Theory and Pattern Recognition Applications , 1996 .
[47] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[48] Léon Personnaz,et al. Neural-network construction and selection in nonlinear modeling , 2003, IEEE Trans. Neural Networks.
[49] Derong Liu,et al. A constructive algorithm for feedforward neural networks with incremental training , 2002 .
[50] Kazuyuki Murase,et al. A New Algorithm to Design Multiple Hidden Layered Artificial Neural Networks , 2006 .
[51] Jooyoung Park,et al. Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.
[52] Tomaso A. Poggio,et al. Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.
[53] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[54] L. Darrell Whitley,et al. Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..
[55] David J. Chalmers,et al. The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .
[56] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[57] Lutz Prechelt,et al. A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice , 1996, Neural Networks.
[58] Mike Wynne-Jones,et al. Node splitting: A constructive algorithm for feed-forward neural networks , 1991, Neural Computing & Applications.
[59] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[60] Xin Yao,et al. Evolving artificial neural networks , 1999, Proc. IEEE.
[61] Lutz Prechelt,et al. PROBEN 1 - a set of benchmarks and benchmarking rules for neural network training algorithms , 1994 .
[62] Md. Monirul Islam,et al. An algorithm for automatic design of two hidden layered artificial neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[63] Lutz Prechelt,et al. Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.
[64] Russell Reed,et al. Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.
[65] Mikko Lehtokangas,et al. Fast initialization for cascade-correlation learning , 1999, IEEE Trans. Neural Networks.
[66] Xin Yao,et al. A Preliminary Study on Designing Artiicial Neural Networks Using Co-evolution , 1995 .
[67] Oded Maimon,et al. A Distributed Genetic Algorithm for Neural Network Design and Training , 1992, Complex Syst..
[68] Christian Lebiere,et al. The Cascade-Correlation Learning Architecture , 1989, NIPS.
[69] Jan Torreele,et al. Temporal Processing with Recurrent Networks: An Evolutionary Approach , 1991, ICGA.
[70] David E. Rumelhart,et al. Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.
[71] Peter J. Angeline,et al. An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.
[72] Pierre Baldi,et al. Temporal Evolution of Generalization during Learning in Linear Networks , 1991, Neural Computation.
[73] Yves Chauvin,et al. Generalization Performance of Overtrained Back-Propagation Networks , 1990, EURASIP Workshop.
[74] Stefan Bornholdt,et al. General asymmetric neural networks and structure design by genetic algorithms: a learning rule for temporal patterns , 1992, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.
[75] Tom Downs,et al. CARVE-a constructive algorithm for real-valued examples , 1998, IEEE Trans. Neural Networks.
[76] Thomas Bäck,et al. Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..
[77] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[78] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[79] Yamashita,et al. Backpropagation algorithm which varies the number of hidden units , 1989 .
[80] Khashayar Khorasani,et al. Constructive feedforward neural networks using Hermite polynomial activation functions , 2005, IEEE Transactions on Neural Networks.
[81] Kazuyuki Murase,et al. An adaptive merging and growing algorithm for designing artificial neural networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[82] M. Mandischer. Evolving recurrent neural networks with non-binary encoding , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.
[83] Albert Y. Zomaya,et al. Toward generating neural network structures for function approximation , 1994, Neural Networks.