Two novel ensemble approaches for improving classification of neural networks

Simple item page
dc.contributor.authorZaamout, Khobaib M
dc.contributor.supervisorZhang, John Z.
dc.date.accessioned2013-02-05T17:15:01Z
dc.date.available2013-02-05T17:15:01Z
dc.date.issued2012
dc.degree.levelMasters
dc.descriptionx, 77 leaves ; 29 cmen_US
dc.description.abstractThe task of pattern recognition is one of the most recurrent tasks that we encounter in our lives. Therefore, there has been a significant interest of automating this task for many decades. Many techniques have been developed to this end, such as neural networks. Neural networks are excellent pattern classifiers with very robust means of learning and a relatively high classification power. Naturally, there has been an increasing interest in further improving neural networks’ classification for complex problems. Many methods have been proposed. In this thesis, we propose two novel ensemble approaches to further improving neural networks’ classification power, namely paralleling neural networks and chaining neural networks. The first seeks to improve a neural network’s classification by combining the outputs of a set of neural networks together via another neural network. The second improves a neural network’s accuracy by feeding the outputs of a neural network into another and continually doing so in a chaining fashion until the error is reduced sufficiently. The effectiveness of both approaches has been demonstrated through a series of experiments. iven_US
dc.identifier.urihttps://hdl.handle.net/10133/3241
dc.language.isoen_USen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2012en_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectPattern recognition systemsen_US
dc.subjectDissertations, Academicen_US
dc.titleTwo novel ensemble approaches for improving classification of neural networksen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ZAAMOUT_KHOBAIB_MSC_2012.pdf
Size:
390.22 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Arts and Science, Faculty of
University of Lethbridge Theses