Long and Medium-Term Research: Information-Based Approachesto Learning Relational Concepts

该奖项是根据长期和中期研究在国外 卓越中心计划，使美国科学家和 工程师在国外进行三到十二个月的研究， 卓越的研究中心。 该计划的奖项 提供联合研究的机会，并利用独特的 或补充设施、专业知识和实验 国外的条件。 该奖项将支持为期三个月的博士后研究访问 由加州大学欧文分校的迈克尔·帕扎尼博士 与悉尼大学的J. Ross Quinlan博士合作。 Pazzani博士将与Quinlan博士在以下方面合作 涉及以信息为基础的关系 概念学习： a. 训练中噪声处理方法的探讨 数据 他将研究最小描述的改进 长度法以及其他处理办法 噪声训练数据。 一个将被探索的替代方案 是对多个模型进行平均的技术。 B。 将关系概念学习应用于更大的问题 自然发生的数据集，确定额外的研究 问题，并检查如何以及复杂性分析在Pazzani &Kibler（出版中）预测了学习方法将如何扩展 更大的问题。 该奖项的建议提供资金，以支付国际 旅行和三个月的津贴

This award is under the Long and Medium-Term Research at Foreign Centers of Excellence Program, which enables U.S. scientists and engineers to conduct three to twelve months of research abroad at research centers of proven excellence. The program's awards provide opportunities for joint research, and the use of unique or complementary facilities, expertise and experimental conditions abroad. This award will support a three-month postdoctoral research visit by Dr. Michael Pazzani of the University of California, Irvine to work with Dr. J. Ross Quinlan of the University of Sydney. Dr. Pazzani will collaborate with Dr. Quinlan on the following topics involving information-based approaches to relational concept learning: A. Investigating approaches to dealing with noise in training data. He will investigate refinements of a Minimum Description Length approach, as well as alternative approaches to dealing with noisy training data. One alternative that will be explored is the technique of averaging multiple models. B. Applying relational concept learning to larger problems on naturally occurring data sets, identifying additional research issues, and examining how well the complexity analysis in Pazzani & Kibler (in press) predicts how the learning methods will scale to larger problems. The award recommendation provides funds to cover international travel and a stipend for three months.