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Abstract

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.

Keywords

Partial Little Square Canonical Correlation Analysis Kernel Method Spectral Cluster Kernel Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tijl De Bie
    • 1
  • Nello Cristianini
    • 2
  1. 1.K.U.Leuven, ESAT-SCDLeuvenBelgium
  2. 2.Statistics Dept.U.C.DavisDavisUSA

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