Date of Graduation
Spring 2018
Degree
Master of Science in Mathematics
Department
Mathematics
Committee Chair
Songfeng Zheng
Abstract
Support Vector Machine(SVM) is a widely-used tool for pattern classification problems. The main idea behind SVM is to separate two different groups with a hyperplane which makes the margin of these two groups maximized. It doesn't require any knowledge about the object we are focused on, since it can catch the features automatically. The idea of SVM can be easily generalized to nonlinear model by a mapping from the original space to a high-dimensional feature space, and they construct a max-margin linear classifier in the high dimensional feature space.
This thesis will investigate the basic idea of SVM and apply its application to multiclass case, that is, one vs. all and one vs. one strategies. As an application, we will apply the scheme to the problem of handwritten digital recognition. We show the difference of performance between linear and non linear technology, in terms of confusing matrix and running time.
Keywords
Support Vector Machine; Multi-class Classification; Linear Model; Non-Linear Classifier; Kernel Function
Subject Categories
Computational Engineering | Robotics
Copyright
© Yu Wang
Recommended Citation
Wang, Yu, "Handwritten Digit Recognition by Multi-Class Support Vector Machines" (2018). MSU Graduate Theses. 3246.
https://bearworks.missouristate.edu/theses/3246
