Kernels and distances for structured data

作     者：Gärtner, T Lloyd, JW Flach, PA 

作者机构：Fraunhofer Inst Autonome Intelligent Syst St Augustin Germany Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England Univ Bonn Dept Comp Sci 3 D-5300 Bonn Germany Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT Australia 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2004年第57卷第3期

页      面：205-232页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：kernel methods structured data inductive logic programming higher-order logic instance-based learning 

摘      要：This paper brings together two strands of machine learning of increasing importance: kernel methods and highly structured data. We propose a general method for constructing a kernel following the syntactic structure of the data, as defined by its type signature in a higher-order logic. Our main theoretical result is the positive definiteness of any kernel thus defined. We report encouraging experimental results on a range of real-world data sets. By converting our kernel to a distance pseudo-metric for 1-nearest neighbour, we were able to improve the best accuracy from the literature on the Diterpene data set by more than 10%.