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Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer

  • Eric Eaton
  • Marie desJardins
  • Terran Lane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5211)

Abstract

In this paper, we propose a novel graph-based method for knowledge transfer. We model the transfer relationships between source tasks by embedding the set of learned source models in a graph using transferability as the metric. Transfer to a new problem proceeds by mapping the problem into the graph, then learning a function on this graph that automatically determines the parameters to transfer to the new learning task. This method is analogous to inductive transfer along a manifold that captures the transfer relationships between the tasks. We demonstrate improved transfer performance using this method against existing approaches in several real-world domains.

Keywords

Transfer Function Logistic Regression Parameter Vector Predictive Accuracy Training Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eric Eaton
    • 1
  • Marie desJardins
    • 1
  • Terran Lane
    • 2
  1. 1.Department of Computer Science and Electrical EngineeringUniversity of Maryland Baltimore County 
  2. 2.Department of Computer ScienceUniversity of New Mexico 

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