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Multilabel Classification in Parallel Tasks

Loza Mencía, Eneldo
Zhang, Min-Ling and Tsoumakas, Grigorios and Zhou, Zhi-Hua (eds.) (2010):
Multilabel Classification in Parallel Tasks.
In: Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010, [Online-Edition: http://www.ke.tu-darmstadt.de/publications/papers/loza10mlpt...],
[Conference or Workshop Item]

Abstract

In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations between domains. Following the multi-task learning setting, in which multiple similar tasks are learned in parallel, we propose a global learning approach that jointly considers all domains. It is empirically demonstrated in this work that this approach is effective despite its simplicity when using a multilabel learner that takes label correlations into account.

Item Type: Conference or Workshop Item
Erschienen: 2010
Editors: Zhang, Min-Ling and Tsoumakas, Grigorios and Zhou, Zhi-Hua
Creators: Loza Mencía, Eneldo
Title: Multilabel Classification in Parallel Tasks
Language: English
Abstract:

In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations between domains. Following the multi-task learning setting, in which multiple similar tasks are learned in parallel, we propose a global learning approach that jointly considers all domains. It is empirically demonstrated in this work that this approach is effective despite its simplicity when using a multilabel learner that takes label correlations into account.

Title of Book: Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010
Divisions: 20 Department of Computer Science > Knowl­edge En­gi­neer­ing
20 Department of Computer Science
Date Deposited: 24 Jun 2011 14:21
Official URL: http://www.ke.tu-darmstadt.de/publications/papers/loza10mlpt...
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