Efficient Kernel Calculation for Multirelational Data

Abstract

Today, most of the data in business applications is stored in relational database systems or in data warehouses built on top of relational database systems. Often, for more data is available than can be processed by standard learning algorithms in reasonable time. This paper presents an extension to kernel algorithms that makes use of the more compact relational representation of data instead of the usual attribute-value representation to significantly speed up the kernel calculation.