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Batch Classification with Applications in Computer Aided Diagnosis

  • Volkan Vural
  • Glenn Fung
  • Balaji Krishnapuram
  • Jennifer Dy
  • Bharat Rao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Abstract

Most classification methods assume that the samples are drawn independently and identically from an unknown data generating distribution, yet this assumption is violated in several real life problems. In order to relax this assumption, we consider the case where batches or groups of samples may have internal correlations, whereas the samples from different batches may be considered to be uncorrelated. Two algorithms are developed to classify all the samples in a batch jointly, one based on a probabilistic analysis and another based on a mathematical programming approach. Experiments on three real-life computer aided diagnosis (CAD) problems demonstrate that the proposed algorithms are significantly more accurate than a naive SVM which ignores the correlations among the samples.

Keywords

Support Vector Machine Pulmonary Embolism Training Point Multiple Instance Learning Mathematical Programming Approach 
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 2006

Authors and Affiliations

  • Volkan Vural
    • 1
  • Glenn Fung
    • 2
  • Balaji Krishnapuram
    • 2
  • Jennifer Dy
    • 1
  • Bharat Rao
    • 2
  1. 1.Department of Electrical and Computer EngineeringNortheastern University 
  2. 2.Computer Aided Diagnosis and Therapy, Siemens Medical SolutionsUSA

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