Modularity, Combining and Artificial Neural Nets

Connection Science

Volumn 9, Issue 1, 1997, Pages 3-10

  Sharkey, Amanda J C ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Artificial neural networks; Combining; Ensembles; Generalization; Modularity; Task decomposition

Indexed keywords

EID: 0031516467     PISSN: 09540091     EISSN: None     Source Type: Journal    
DOI: 10.1080/095400997116702     Document Type: Article

References (22)

1. Baxt, W.G. (1990) Use of an artificial neural network for data analysis in clinical decision making: the diagnosis of acute coronary occlusion. Neural Computation, 2, 480-489.
2. Bennani, Y. (1995) A modular and hybrid connectionist system for speaker identification. Neural Computation, 7, 791-798.
3. Bourlard, H. & Morgan, N. (1993) Connectionist Speech Recognition: A Hybrid Approach. Norwell, MA: Kluwer.
4. Chalmers, D.J. (1990) Syntactic transformation on distributed representations. Connection Science, 2, 53-62.
5. de Bollivier, M., Gallinari, P. & Thiria, S. (1991) Neural nets and task decomposition. In Proceedings of the International Joint Conference on Neural Networks, Seattle, IEEE, Vol. 2, pp. 573-576.
6. Fogelman, F., Lamy, B. & Viennet, E. (1993) Multimodular neural network architectures for pattern recognition: applications in optical character recognition and human face recognition. In I. Guyon & P. Wang (Eds), Advances in Pattern Recognition Using Neural Network Technologies. River Edge, NJ: World Scientific.
7. Gallinari, P. (1995) Modular neural net systems, training of. In M.A. Arbib (Ed.) The Handbook of Brain Theory and Neural Networks. London: Bradford Books/MIT Press, pp. 582-585.
8. Hampshire, J. & Waibel, A. (1989) The Meta-Pi network: Building distributed Knowledge Representation for Robust Pattern Recognition, Technical Report CMU-CS-89-166. Pittsburgh, PA: Carnegie Mellon University.
9. Hashem, S. (1996) Effects of collinearity on combining neural networks. Connection Science, 8, 315-336.
10. Hrycej, T. (1992) Modular Learning in Neural Networks. Chichester: John Wiley.
11. Jacobs, R.A., Jordan, M.I., Nowlan, S.J. & Hinton, G.E. (1991) Adaptive mixtures of local experts. Neural Computation, 3, 79-97.
12. Jordan, M.I. & Jacobs, R.A. (1994) Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.
13. Jordan, M.I. & Jacobs, R.A. (1995) Modular and hierarchical learning systems. In M.A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks. London: Bradford Books/MIT Press, pp. 579-581.
14. Kim, K. & Barlett, E.B. (1995) Error estimation by series association for neural network systems. Neural Computation, 7, 799-808.
15. Pratt, L.Y., Mostow, J. & Kamm, C.A. (1991) Direct transfer of learned information among neural networks. In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, CA, pp. 584-589.
16. Rosen, B.E. (1996) Ensemble learning using decorrelated neural networks. Connection Science, 8, 373-384.
17. Sharkey, A.J.C. (1996) On combining artificial neural nets. Connection Science, 8, 299-314.
18. Sharkey, N.E. & Sharkey, A.J.C. (1992) A modular design for connectionist parsing. In M.F.J. Drosaers & A. Nijholt (Eds), Proceedings of Workshop on Language Technology, Twente.
19. Thiria, S., Mejia, C., Badran, F. & Crepon, M. (1992) Multimodular architecture for remote sensing operations. In J.E. Moody, S.J. Hanson & R.P. Lippmann (Eds), Advances in Neural Information Processing Systems 4. San Mateo, CA: Morgan Kaufmann, pp. 675-682.
20. Turner, K. & Ghosh, J. (1996) Error correlation and error reduction in ensemble classifiers. Connection Science, 8, 385-404.
21. von der Malsburgh, S. (1987) Synaptic pasticity as a basis of brain organisation. In J.P. Changeux & M. Konishi (Eds), The Neural and Molecular Bases of Learning. New York: Wiley.
22. Waibel, A., Sawai, J. & Shikano, K. (1989) Modularity and scaling in large phonemic neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing, 37, 1888-1898.