Case-based statistical learning: A non parametric implementation applied to SPECT images

Abstract

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 nonparametric 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 classhypothesis 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.