PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Hierarchical Multi-Category Text Classification Models
Juho Rousu, Craig Saunders, Sandor Szedmak and John Shawe-Taylor
In: 22nd International Conference on Machine Learning (ICML 2005), 7-11 August, 2005, Bonn, Germany.


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. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. The algorithm's predictive accuracy is competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1049
Deposited By:Craig Saunders
Deposited On:18 August 2005