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