The Open Cybernetics & Systemics Journal
2008, 2 : 101-105Published online 2008 April 23. DOI: 10.2174/1874110X00802010101
Publisher ID: TOCSJ-2-101
New Criteria for the Linear Binary Separability in the Euclidean Normed Space
Department of Electrical
Engineering, I-Shou University, Kaohsiung, Taiwan 840, Republic of
China.
ABSTRACT
In this paper, the classical binary classification problem is investigated. Necessary and sufficient criterion is presented to guarantee the linear binary separability of the training data in the Euclidean normed space. A suitable hyperplane that correctly classifies the training data is also constructed provided that the necessary and sufficient is satisfied. Based on the main result, we offer an easy-to-check criterion for the linear binary separability of the training set. Finally, a numerical example is provided to illustrate the feasibility and effectiveness of the obtained result.