Kernel-Based Learning of  Hierarchical Multilabel Classification Models

Kernel-Based Learning of Hierarchical Multilabel Classification Models (2006)

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by Juho Rousu , Craig Saunders , Sandor Szedmak , John Shawe-Taylor

Venue:	JOURNAL OF MACHINE LEARNING RESEARCH
Citations:	33 - 5 self

BibTeX

@ARTICLE{Rousu06kernel-basedlearning,
    author = {Juho Rousu and Craig Saunders and Sandor Szedmak and John Shawe-Taylor},
    title = {Kernel-Based Learning of Hierarchical Multilabel Classification Models},
    journal = {JOURNAL OF MACHINE LEARNING RESEARCH},
    year = {2006},
    volume = {7},
    pages = {1601--1626}
}

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Abstract

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set. Experiments show

Citations

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