Machine Learning-Based Small Cell Location Selection Process

Correlation-based Feature Selection for Machine Learning

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 problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated w...

Machine Learning for Subproblem Selection

Subproblem generation, solution, and recombination is a standard approach to combinatorial optimization problems. In many settings identifying suitable subproblems is itself a significant component of the technique. Such subproblems are often identified using a heuristic rule. Here we show how to use machine learning to make this identification. In particular we use a learned objective function...

Machine Learning Based Cooperative Relay Selection in Virtual MIMO

In cellular systems, virtual multiple-input multiple-output (V-MIMO) technology promises to achieve performance gains comparable to conventional MIMO. In this paper, we propose cooperative relay selection algorithm based on machine learning techniques. Willingness of user to cooperate in V-MIMO depends on his current battery power, time and day along with incentives offered by service provider....

Process-Oriented User Behavior Study Based on Machine Learning

Machine Learning based on Software Metrics for Process Assessment

The improvement of the currently used processes and quality assurance mechanisms is an important part of software engineering. In our work, we apply machine learning techniques to metric data with the aim to provide techniques that improve the state of the art. Machine learning has the advantage of being unbiased, whereas experts instinctively use their intuition and expertise, which may be bia...