On Maximum Margin Hierarchical Classification
On Maximum Margin Hierarchical Classification
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.
Rousu, J.
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Saunders, C.
38a38da8-1eb3-47a8-80bc-b9cbb43f26e3
Szedmak, S.
993ca93f-c7c7-4d0b-a5f7-374eb0401add
Shawe-Taylor, J.
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2004
Rousu, J.
4ef21914-6600-483b-9995-ff0283dad7b3
Saunders, C.
38a38da8-1eb3-47a8-80bc-b9cbb43f26e3
Szedmak, S.
993ca93f-c7c7-4d0b-a5f7-374eb0401add
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Rousu, J., Saunders, C., Szedmak, S. and Shawe-Taylor, J.
(2004)
On Maximum Margin Hierarchical Classification.
Workshop on Learning with Structured Outputs at NIPS 2004, Whistler.
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Abstract
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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M3_NIPS04.pdf
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Published date: 2004
Additional Information:
Event Dates: December 2004
Venue - Dates:
Workshop on Learning with Structured Outputs at NIPS 2004, Whistler, 2004-12-01
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 260253
URI: http://eprints.soton.ac.uk/id/eprint/260253
PURE UUID: 1a3531e9-9f34-47ae-ad91-488f6d7b7af2
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Date deposited: 11 Jan 2005
Last modified: 14 Mar 2024 06:34
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Contributors
Author:
J. Rousu
Author:
C. Saunders
Author:
S. Szedmak
Author:
J. Shawe-Taylor
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