Model Selection

In this chapter, we investigate the issue of model selection and the use of the nonconformity (strangeness) measure in batch learning. Using the nonconformity measure, we propose a new training algorithm that helps avoid the need for Cross-Validation or Leave-One-Out model selection strategies. We provide a new generalization error bound using the notion of nonconformity to upper bound the loss of each test example and show that our proposed approach is comparable to standard model selection methods, but with theoretical guarantees of success and faster convergence. We demonstrate our novel model selection technique using the Support Vector Machine algorithm.