@inproceedings{020d9c2f98b84710a1d3eedbf19f53c2,
title = "Kernels for structured data",
abstract = "Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.",
author = "Thomas G{\"a}rtner and Lloyd, {John W.} and Flach, {Peter A.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.; 12th International Conference on Inductive Logic Programming, ILP 2002 ; Conference date: 09-07-2002 Through 11-07-2002",
year = "2003",
doi = "10.1007/3-540-36468-4_5",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "66--83",
editor = "Stan Matwin and Claude Sammut",
booktitle = "Inductive Logic Programming",
address = "Germany",
}