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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


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



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


Research Data and Supplementary Material