Inductive Learning using Theory-Refinement Knowledge-Based Artificial Neural Network

이론정련 지식기반인공신경망을 이용한 귀납적 학습

  • 심동희 (전주대학교 정보기술컴퓨터공학부)
  • Published : 2001.06.01


Since KBANN (knowledge-based artificial neural network) combing the inductive learning algorithm and the analytical learning algorithm was proposed, several methods such as TopGen, TR-KBANN, THRE-KBANN which modify KBANN have been proposed. But these methods can be applied when there is a domain theory. The algorithm representing the problem into KBANN based on only the instances without domain theory is proposed in this paper. Domain theory represented into KBANN can be refined by THRE-KBANN. The performance of this algorithm is more efficient than the C4.5 in the experiment for some problem domains of inductive learning.