00000cab a22000003a 4500 UP-99796217608796381 Buklod 20231007234805.0 m |o d | ta 090422s xx d | ||r ||||| || DENGII eng Gartner, Thomas Kernels and Distances for Structured Data. pp. 205-232 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%. Kernel methods. Structured data. Inductive logic programming. Higher order logic. Instance based learning. Machine learning. 57, 3 (2004). FO UPD DENG-II Article