Simultaneous evolution of neural network topologies and weights for classification and regression

Lecture Notes in Computer Science

Volumn 3512, Issue , 2005, Pages 59-66

  Rocha, Miguel ^a   Cortez, Paulo ^a   Neves, José ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

Ensembles; Evolutionary Algorithms; Multilayer Perceptrons; Supervised Learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); MAPPING; OPTIMIZATION; REGRESSION ANALYSIS;

INPUT-OUTPUT MAPPING; LOCAL MINIMA;

NEURAL NETWORKS;

EID: 25144460534     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11494669_8     Document Type: Conference Paper

References (17)

1. P. Cortez, M. Rocha, and J. Neves. A Lamarckian Approach for Neural Network Training. Neural Processing Letters, 15(2):105-116, April 2002.
2. T. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, Multiple Classifier Systems, LNCS 1857, pages 1-15. Springer, 2001.
3. S. Haykin. Neural Networks - A Compreensive Foundation. Prentice-Hall, New Jersey, 2nd edition, 1999.
4. R. Kohavi. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Quebec, Canada, August 1995.
5. T. Kwok and D. Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems problems: A survey. IEEE Transactions on Neural Networks, 8(3):630-645, May 1999.
6. Y. Liu and X. Yao. Evolving Modular Neural Networks Which Generalize Well. In Proc. of the 1997 IEEE Intern. Confer, on Evolutionary Computation, Indianapolis, pages 670-675, New York, 1997. IEEE Press.
7. Y. Liu, X. Yao, and T. Higuchi. Evolutionary Ensembles with Negative Correlation Learning. IEEE Transactions on Evolutionary Computation, 4(4):380-387, 2000.
8. J.R. Quinlan. Comparing Connectionist and Symbolic Learning Methods, pages 445-456. MIT Press, Cambridge, Massachustess, 1994.
9. M. Riedmiller. Supervised Learning in Multilayer Perceptrons - from Backpropagation to Adaptive Learning Techniques. Computer Standards and Interfaces, 16, 1994.
10. M. Rocha, P. Cortez, and J. Neves. Evolutionary Neural Network Learning. In F. Pires and S. Abreu, editors, Progress in Artificial Intelligence, EPIA 2003 Proceedings, LNAI 2902, pages 24-28, Beja, Portugal, December 2003. Springer.
11. M. Rocha, P. Cortez, and J. Neves. Ensembles of Artificial Neural Networks with Heterogeneous Topologies. In Proceedings of the 4th Symposium on Engineering of Intelligent Systems (EIS2004). ICSC Academic Press, March 2004.
12. M. Rocha, P. Cortez, and J. Neves. Evolutionary Design of Neural Networks for Classification and Regression. In ICANNGA Proceedings, Coimbra, Portugal, March, 2005. Springer.
13. C. Scares. Is the UCI Repository Useful for Data Mining? In F. Pires and S. Abreu, editors, Progress in Artificial Intelligence, EPIA 2003 Proceedings, LNAI 2902, pages 209-223, Beja, Portugal, 2003. Springer.
14. G. Thimm and E. Fiesler. Evaluating pruning methods. In Proc. of the Int. Symp. on Artificial Neural Networks, pages 20-25, Taiwan, December 1995.
15. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, USA, 2000.
16. X. Yao. Evolving Artificial Neural Networks. In Proc. of the IEEE, 87(9): 1423-1447, September 1999.
17. X. Yao and Y. Liu. A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks, 8(3):694-713, 1997.