Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

Abstract Machine learning studies automatic algorithms that improve themselves through
experience. It is widely used for analyzing and extracting value from large biomedical data …

A great deal of attention has been given to deep learning over the past several years, and
new deep learning techniques are emerging with improved functionality. Many computer …

Background Modern modelling techniques may potentially provide more accurate
predictions of binary outcomes than classical techniques. We aimed to study the predictive …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

A central problem in machine learning is identifying a representative set of features from
which to construct a classification model for a particular task. This thesis addresses the …

In this survey, we review work in machine learning on methods for handling data sets
containing large amounts of irrelevant information. We focus on two key issues: the problem …

Learning curves are a concept from social sciences that has been adopted in the context of
machine learning to assess the performance of a learning algorithm with respect to a certain …

For large, real-world inductive learning problems, the number of training examples often
must be limited due to the costs associated with procuring, preparing, and storing the …

Metaheuristics are a relatively new but already established approach to combinatorial
optimization. A metaheuristic is a generic algorithmic template that can be used for finding …