Browse by Subject "Machine Learning"
Now showing items 25-32 of 32
(2004-09)Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive ...
(2014-09-16)Humans can understand scenes with abundant detail: they see layouts, surfaces, the shape of objects among other details. By contrast, many machine-based scene analysis algorithms use simple representation to parse scenes, ...
(2013-05-24)The problem of ascribing a semantic representation to text is an important one that can help text understanding problems like textual entailment. In this thesis, we address the problem of assigning a shallow semantic ...
(2014-01-16)Real-world data entities are often connected by meaningful relationships, forming large-scale networks. With the rapid growth of social networks and online relational data, it is widely recognized that networked data are ...
(2009)Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) ...
(2012-02-06)In order for a machine learning effort to succeed, an appropriate model must be chosen. This is a difficult task in which one must balance flexibility, so that the model can capture the complexities of the domain, and ...
(2013-08-22)This thesis deals with incorporating artificial intelligence into a humanoid robot by making a cognitive model of the learning process. The goal is to “teach” a specialized humanoid robot, the iCub robot, to solve any ...
(2012-09-18)Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems ...