Hybrid methods using evolutionary algorithms for on-line training
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George D. Magoulas | Vassilis P. Plagianakos | Michael N. Vrahatis | M. N. Vrahatis | G. D. Magoulas | V. Plagianakos
[1] Sung Wook Baik,et al. Adaptive object recognition based on the radial basis function paradigm , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[2] Thomas Bäck,et al. An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.
[3] R.M. Haralick,et al. Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.
[4] PoliRiccardo,et al. Evolving the Topology and the Weights of Neural Networks Using a Dual Representation , 1998 .
[5] George D. Magoulas,et al. Effective Backpropagation Training with Variable Stepsize , 1997, Neural Networks.
[6] Richard S. Sutton,et al. Online Learning with Random Representations , 1993, ICML.
[7] Phil Brodatz,et al. Textures: A Photographic Album for Artists and Designers , 1966 .
[8] Graeme G. Wilkinson,et al. Open Questions in Neurocomputing for Earth Observation , 1997 .
[9] Thibault Langlois,et al. Parameter adaptation in stochastic optimization , 1999 .
[10] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[11] G. D. Magoulas,et al. Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information , 2000, Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy.
[12] Terence C. Fogarty,et al. A Comparative Study of Steady State and Generational Genetic Algorithms , 1996, Evolutionary Computing, AISB Workshop.
[13] Stefanos Kollias,et al. Neural network-assisted effective lossy compression of medical images , 1996, Proc. IEEE.
[14] Nicol N. Schraudolph,et al. Local Gain Adaptation in Stochastic Gradient Descent , 1999 .
[15] Nicol N. Schraudolph,et al. Online Local Gain Adaptation for Multi-Layer Perceptrons , 1998 .
[16] Vassilis P. Plagianakos,et al. Training neural networks with threshold activation functions and constrained integer weights , 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.
[17] Peter J. Angeline,et al. Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.
[18] Ralf Salomon,et al. Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments , 1997, Artificial Evolution.
[19] Shumeet Baluja,et al. Evolution of an artificial neural network based autonomous land vehicle controller , 1996, IEEE Trans. Syst. Man Cybern. Part B.
[20] Sung Wook Baik,et al. Adaptive RBF classifier for object recognition in image sequences , 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.
[21] Vassilis P. Plagianakos,et al. Learning in multilayer perceptrons using global optimization strategies , 2001 .
[22] David Saad,et al. On-Line Learning in Neural Networks , 1999 .
[23] M. N. Vrahatis,et al. Adaptive stepsize algorithms for on-line training of neural networks , 2001 .
[24] Stefanos D. Kollias,et al. On-line retrainable neural networks: improving the performance of neural networks in image analysis problems , 2000, IEEE Trans. Neural Networks Learn. Syst..
[25] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..