The development of learning algorithms for structured data, i.e. data that cannot be represented by numerical vectors, is a relevant challenge in machine learning. Kernel Methods, which is a leading machine learning technology for vectorial data, recently tackled the structured data. In this paper we focus our attention on Kernel Methods that face up to data that can be represented by means of graphs, by providing an in-depth review through a comprehensive approach to the research hints and the main open problems in this area of research.

KERNEL METHODS FOR GRAPHS: A COMPREHENSIVE APPROACH

CAMASTRA, Francesco;PETROSINO, Alfredo
2008

Abstract

The development of learning algorithms for structured data, i.e. data that cannot be represented by numerical vectors, is a relevant challenge in machine learning. Kernel Methods, which is a leading machine learning technology for vectorial data, recently tackled the structured data. In this paper we focus our attention on Kernel Methods that face up to data that can be represented by means of graphs, by providing an in-depth review through a comprehensive approach to the research hints and the main open problems in this area of research.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/23585
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