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. © Springer-Verlag 2004.
CITATION STYLE
De Bie, T., & Cristianini, N. (2004). Kernel methods for exploratory pattern analysis: A demonstration on text data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3138, 16–29. https://doi.org/10.1007/978-3-540-27868-9_2
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