Case-Based Statistical Learning: A Non Parametric Implementation Applied to SPECT Images

  • J. M. GórrizEmail author
  • J. Ramírez
  • F. J. Martinez-Murcia
  • I. A. Illán
  • F. Segovia
  • D. Salas-González
  • A. Ortiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)


In the theory of semi-supervised learning, we have a training set and a unlabeled data that are employed to fit a prediction model or learner with the help of an iterative algorithm such as the expectation-maximization (EM) algorithm. In this paper a novel non-parametric approach of the so called case-based statistical learning in a low-dimensional classification problem is proposed. This supervised model selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. The estimation of the error rates from a well-trained SVM allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test.


Statistical learning and decision theory Support vector machines (SVM) Hypothesis testing Partial least squares Conditional-error rate 



This work was partly supported by the MINECO under the TEC2015-64718-R project and the Consejería de Economía, Inno- vación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. M. Górriz
    • 1
    Email author
  • J. Ramírez
    • 1
  • F. J. Martinez-Murcia
    • 1
  • I. A. Illán
    • 3
  • F. Segovia
    • 1
  • D. Salas-González
    • 1
  • A. Ortiz
    • 2
  1. 1.Department of Signal Theory and CommunicationsUniversity of GranadaGranadaSpain
  2. 2.Department of Communications EngineeringUniversidad de MalagaMálagaSpain
  3. 3.Department of Scientific ComputingThe Florida State UniversityTallahasseeUSA

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