Publications on Neural Intelligence

• Combining locally trained neural networks by introducing a reject class, Kim, S.-J. and Zhang, B.-T., Proceedings of the International Joint Conference on Neural Networks (IJCNN'99), 6:4043-4047, 1999. [PDF]
• Active data partitioning for building mixture models, Kim, S.-J. and Zhang, B.-T., Proceedings of the International Conference on Neural Information Processing (ICONIP'98), 2:854-857, 1998.
• Distributed parallel cooperative problem-solving with a voting and election system of neural networks, Zhang, B.-T., Parallel Processing in Neural Systems and Computers, pp. 513-516, 1990.

• Learning constructive RBF networks by active data selection, Park, S.-W. and Zhang, B.-T., Proceedings of the International Conference on Neural Information Processing (ICONIP-2000), 2:1411-1415, 2000. [PDF]
• Accelerated learning by active example selection, Zhang, B.-T., International Journal of Neural Systems, 5(1):67-75, 1994. [PDF]
• Active learning algorithms for neural networks, Zhang, B.-T., Proceedings of the First International Conference on Neural Information Processing (ICONIP'94), 3:1720-1725, 1994.
• Selecting a critical subset of given examples during learning, Zhang, B.-T., Proceedings of the International Conference on Artificial Neural Networks (ICANN'94), pp. 517-520, Springer-Verlag, 1994.
• Teaching neural networks by genetic exploration, B.T. Zhang, Arbeitspapiere der GMD, No 805, German National Research Center for Computer Science (GMD), St. Augustin/Bonn, November 1993.
• Learning by incremental selection of critical examples, Zhang, B.-T., Arbeitspapiere der GMD, No 735, German National Research Center for Computer Science (GMD), St. Augustin/Bonn, March 1993.
• Neural networks that teach themselves through genetic discovery of novel examples, Zhang B.-T. and Veenker, G., Proceedings of the 1991 IEEE International Joint Conference on Neural Networks (IJCNN'91), 1:690-695, 1991. [PDF]
• Focused incremental learning for improved generalization with reduced training sets, Zhang B.-T. and Veenker, G., Proceedings of the International Conference on Artificial Neural Networks (ICANN'91), pp. 227-232, 1991.

• An incremental learning algorithm that optimizes network size and sample size in one trial, Zhang, B.-T., Proceedings of IEEE International Conference on Neural Networks (ICNN'94), 1:215-220, 1994. [PDF]
• Self-development learning: constructing optimal size neural networks via incremental data selection, Zhang, B.-T., Arbeitspapiere der GMD, No 768, German National Research Center for Computer Science (GMD), St. Augustin/Bonn, July 1993.
• Learning by Genetic Neural Evolution (in German), Zhang, B.-T., PhD Dissertation, University of Bonn, Germany, published as DISKI Series, vol. 16, ISBN 3-929037-16-6, 268 pages, Infix-Verlag, St. Augustin/Bonn, July 1992.

• Temporal pattern recognition using a spiking neural network with delays, Sohn, J.-W., Zhang, B.-T., and Kaang, B.-K., Proceedings of the International Joint Conference on Neural Networks (IJCNN'99), 4:2590-2593, 1999. [PDF]

• Word sense disambiguation by learning decision trees from unlabeled data, Park, S.-B., Zhang, B.-T., and Kim, Y. T., Applied Intelligence, 19(1):27-38, 2003. [PDF]
• Boosting linear perceptrons for unbalanced data, O, Jangmin and Zhang, B.-T., Proceedings of the International Conference on Neural Information Processing (ICONIP-2000), 1:642-645, 2000. [PDF]
• Text filtering by boosting naive Bayes classifiers, Kim, Y.-H., Hahn, S.-Y., and Zhang, B.-T., Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2000), pp. 168-175, 2000. [PDF]