Description

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances.

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented.

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    Date Created
    • 2011
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  • Text
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    • Partial requirement for: M.S., Arizona State University, 2011
      Note type
      thesis
    • Includes bibliographical references (p
    • Field of study: Computer science

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    by Ashok Venkatesan

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