Rule Extraction from Knowledge-Based Neural Networks with Adaptive Inductive Bias
The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving realworld applications. The paradigm provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guides network training, and to extract knowledge from trained networks. The role of neural networks then becomes that of knowledge refinement. It thus provides a methodology for dealing with uncertainty in the prior knowledge. We have previously proposed a heuristic for determining the strength of the inductive bias which takes the network architecture, the prior knowledge, the training data, and the learning algorithm into consideration; networks trained with adaptive inductive bias showed superior performance over networks trained with a standard inductive bias. This paper compares the performance of symbolic rules extracted from networks trained with and without adaptive bias, respectively. We give empirical results for a difficult problem in molecular biology.
Knowledge-Based Neural Networks Inductive Bias Rule Extraction Molecular Biology.
Sean Snyders Christian W. Omlin
Department of Computer Science University of Stellenbosch 7600 Stellenbosch, South Africa Department of Computer Science University of the Western Cape 7535 Bellville, South Africa
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
181-186
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）