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Multi-task learning using shared and task specific information

Srijith, PK and Shevade, Shirish (2012) Multi-task learning using shared and task specific information. In: ICONIP 2012 19th International Conference, November 12-15, 2012, Doha, Qatar.

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Official URL: http://dx.doi.org/10.1007/978-3-642-34487-9_16

Abstract

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Springer.
Keywords: Multi-Task Learning; Support Vector Machines; Kernel Methods; Sparse Models
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Francis Jayakanth
Date Deposited: 20 Nov 2013 11:47
Last Modified: 20 Nov 2013 11:53
URI: http://eprints.iisc.ac.in/id/eprint/47813

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