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On Maximum Margin Hierarchical Classification

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

Record type: Conference or Workshop Item (Other)


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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Published date: 2004
Additional Information: Event Dates: December 2004
Venue - Dates: Workshop on Learning with Structured Outputs at NIPS 2004, 2004-12-01
Organisations: Electronics & Computer Science


Local EPrints ID: 260253
PURE UUID: 1a3531e9-9f34-47ae-ad91-488f6d7b7af2

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Date deposited: 11 Jan 2005
Last modified: 18 Jul 2017 09:14

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Author: J. Rousu
Author: C. Saunders
Author: S. Szedmak
Author: J. Shawe-Taylor

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