PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Maximum Margin Hierarchical Multilabel Classification
Juho Rousu, Craig Saunders, Sandor Szedmak and John Shawe-Taylor
In: NIPS'2004 Workshop on Learning with Structured Outputs, 18 Dec 2004, Whistler, Canada.


We present work in progress towards maximum margin hierarchical classification where the objects are allowed to belong to more than one category at a time. The classification hierarchy is represented as a Markov network equipped with an exponential family defined on the edges. We present a variation of the maximum margin multilabel learning framework, suited to the hierarchical classification task and allows efficient implementation via gradient-based methods. We compare the behaviour of the proposed method to the recently introduced hierarchical regularized least squares classifier as well as two SVM variants in Reuter's news article classification.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:464
Deposited By:Juho Rousu
Deposited On:23 December 2004