Exploiting Background Knowledge when Learning Similarity Measures

The definition of similarity measures—one core component of every CBR application—leads to a serious knowledge acquisition problem if domain and application specific requirements have to be considered. To reduce the knowledge acquisition effort, different machine learning techniques have been developed in the past. In this paper, enhancements of our framework for learning knowledge-intensive similarity measures are presented. The described techniques aim to restrict the search space to be considered by the learning algorithm by exploiting available background knowledge. This helps to avoid typical problems of machine learning, such as overfitting the training data.

• Type: Conference
• Year: 2004
• Where: EWCBR
• Authors: Thomas Gabel, Armin Stahl
• Keywords: Artificial Intelligence, EWCBR 2004, Knowledge Acquisition, Knowledge Acquisition Problem, Machine Learning