標題: Machine learning by imitating human learning
作者: Chang, KC
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: machine learning;human learning;rule;training instance;specialize;generalize
公開日期: 1-五月-1996
摘要: Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments.
URI: http://hdl.handle.net/11536/1328
ISSN: 0924-6495
期刊: MINDS AND MACHINES
Volume: 6
Issue: 2
起始頁: 203
結束頁: 228
顯示於類別:期刊論文


文件中的檔案:

  1. A1996VB19100004.pdf