| 摘要: |
| 传统的解释学习(EBL)是通过单个实例进行学习的，学习结果往往带有实例本身的特殊性质,知识求精能较正这一缺陷，但学习结果的效用不高．本文结合了EBL方法和求精算法，提出综合多个实例的增量式解释学习算法EBG—plus，学习质量随实例数目增加而单调上升，学习结果效用高，并能够自动改进领域知识的编码质量． |
| 关键词: 解释学习，知识求精，增量式学习． |
| DOI： |
| 分类号: |
| 基金项目:本文得到国家自然科学基金资助. |
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| INCREMENTAL EXPLANATION—BASED LEARNING ALGORITHM EBG—PLUS |
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Hao Jigang,Shi Chunyi
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| Abstract: |
| Explanation—based learning(EBL)methods learn from single training ex-ample．The learning result often bears the example's own speciality．The knowledge re-finement algorithm can rectify the speciality in EBL，but with rather low utility．This pa-per combines EBG and refinement algorithm，gives an incremental learning algorlthm——EBG—plus，which can take advantage of many examples．While maintaining high utility，the authors get better result as new instances are met．By the way，the quality of domain knowledge can be automatically improved． |
| Key words: EBL,knowledge refinetnent,incremental learning． |
