Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data

Kernel Methods are a class of algorithms for pattern analysis with a number of convenient features. They can deal in a uniform way with a multitude of data types and can be used to detect many types of relations in data. Importantly for applications, they have a modular structure, in that any kernel function can be used with any kernel-based algorithm. This means that customized solutions can be easily developed from a standard library of kernels and algorithms. This paper demonstrates a case study in which many algorithms and kernels are mixed and matched, for a cross-language text analysis task. All the software is available online.

• Type: Conference
• Year: 2004
• Where: SSPR
• Authors: Tijl De Bie, Nello Cristianini
• Keywords: Kernel Methods, Pattern Analysis, Pattern Recognition, SSPR 2004, Uniform Way