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Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

  • Chuan Lu
  • Tony Van-Gestel
  • Johan A. K. Suykens
  • Sabine Van-Huffel
  • Dirk Timmerman
  • Ignace Vergote
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

The aim of this study is to develop the Bayesian LeastSquares Support Vector Machine (LS-SVM) classifiers for preoperative discrimination between benign and malignant ovarian tumors. We describe how to perform (hyper)parameter estimation, input variable selection for LS-SVMs within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. The performance of the LS-SVM models with linear and RBF kernels has been evaluated and compared with Bayesian multi-layer perceptrons (MLPs) and linear discriminant analysis.

Keywords

Support Vector Machine Ovarian Tumor Little Square Support Vector Machine Malignant Ovarian Tumor Model Evidence 
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 2003

Authors and Affiliations

  • Chuan Lu
    • 1
  • Tony Van-Gestel
    • 1
  • Johan A. K. Suykens
    • 1
  • Sabine Van-Huffel
    • 1
  • Dirk Timmerman
    • 2
  • Ignace Vergote
    • 2
  1. 1.Dept. of Electrical EngineeringKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Dept. of Obstetrics and GynecologyUniversity Hospitals LeuvenLeuvenBelgium

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