Rousu, J., Saunders, C., Szedmak, S. and Shawe-Taylor, J.
On Maximum Margin Hierarchical Classification.
At Workshop on Learning with Structured Outputs at NIPS 2004, Whistler,
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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