Term of Award
Fall 2018
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Ionut Iacob
Committee Member 1
Mehdi Allahyari
Committee Member 2
Stephen Carden
Committee Member 3
Goran Lesaja
Committee Member 3 Email
goran@georgiasouthern.edu
Abstract
In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes left in the process. In the best case scenario, our algorithm makes a final decision between k classes in O(log2 k) decision steps and in the worst case scenario, DCSVM makes a final decision in k - 1 steps.
OCLC Number
1085541962
Recommended Citation
Rathgamage Don, Duleep Prasanna W., "Multiclass Classification Using Support Vector Machines" (2018). Electronic Theses and Dissertations. 1845.
https://digitalcommons.georgiasouthern.edu/etd/1845
Research Data and Supplementary Material
No
Included in
Artificial Intelligence and Robotics Commons, Other Applied Mathematics Commons, Other Statistics and Probability Commons
