New Criteria for the Linear Binary Separability in the Euclidean Normed Space
Yeong‐Jeu Sun · The Open Cybernetics & Systemics Journal · 2008
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.