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JMLR
2006

Kernel-Based Learning of Hierarchical Multilabel Classification Models

9 years 11 months ago
Kernel-Based Learning of Hierarchical Multilabel Classification Models
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 that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light classifiers for each node. The algorithm's predictive accuracy was found to be comp...
Juho Rousu, Craig Saunders, Sándor Szedm&aa
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
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