Paper Info
Title
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 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.
Year
DOI
Venue
2017
10.1007/978-3-319-59740-9_30
Lecture Notes in Computer Science
Keywords
Field
DocType
Statistical learning and decision theory,Support vector machines (SVM),Hypothesis testing,Partial least squares,Conditional-error rate
Computer vision,Likelihood-ratio test,Computer science,Iterative method,Support vector machine,Model selection,Nonparametric statistics,Gaussian,Artificial intelligence,Probability density function,Statistical hypothesis testing,Machine learning
Conference
Volume
ISSN
Citations 
10337
0302-9743
0
PageRank 
References 
Authors
0.34
8
7
Name
Order
Citations
PageRank
Juan Manuel Górriz Sáez128935.14
Javier Ramírez265668.23
Francisco Jesús Martínez-Murcia37417.08
I. Illán4719.24
Fermín Segovia57914.71
D. Salas-Gonzalez631226.61
Andrés Ortiz719525.64