Refining Concepts And Domain Theories By Combining Explanation-Based And Empirical Learning

最近，在机器学习研究中出现了两种普遍的方法。经验学习包括通过检查大量例子之间的异同来归纳性地获得概念描述。基于解释的学习(EBL)涉及到利用现有知识来解释和概括单个例子，从而获得可操作的概念描述。然而，这两种方法都有很大的弱点。标准的经验方法不能充分利用现有知识。基于解释的标准方法不能处理不完整或不正确的领域理论。我们的目标是开发稳健和高效的方法，将基于解释的技术和经验技术相结合，以提炼不完美的概念和领域理论，以解释一组经验数据。

Recently, two general approaches have emerged in machine learning research. Empirical learning involves inductively acquiring concept descriptions by examining the similarities and differences among a large number of examples. Explanation-based learning (EBL) involves using existing knowledge to explain and generalize single examples and thereby acquire operational concept descriptions. However, both of these approaches have important weaknesses. Standard empirical methods cannot take adequate advantage of existing knowledge. Standard explanation- based methods cannot deal with domain theories which are incomplete or incorrect. Our goal is to develop robust and efficient methods that combine explanation-based and empirical techniques to refine imperfect concepts and domain theories to account for a set of empirical data.