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Multi-Task Learning Using Shared and Task Specific Information

  • P. K. Srijith
  • Shirish Shevade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7665)

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.

Keywords

Multi-task learning Support vector machines Kernel methods Sparse models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • P. K. Srijith
    • 1
  • Shirish Shevade
    • 1
  1. 1.Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

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