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Multilabel Classification in Parallel Tasks
Type of publication: Inproceedings
Citation: loza10pt
Booktitle: Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010
Year: 2010
Month: June
Pages: 29-36
Location: Haifa, Israel
Crossref: zhang10mlworkshop
URL: http://www.ke.tu-darmstadt.de/publications/papers/loza10mlpt.pdf
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.
Keywords:
Authors Loza MencĂ­a, Eneldo
Editors Zhang, Min-Ling
Tsoumakas, Grigorios
Zhou, Zhi-Hua
Attachments
  • http://cse.seu.edu.cn/conf/MLD...
       (full proceedings)
  • http://cse.seu.edu.cn/conf/MLD...
       (slides at workshop)
Topics