Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.

Effect of the Edge Type and Strain on the Structural, Electronic and Magnetic Properties of the BNRs

We present the effect of edge structures on the edge energy and stress of BN nanoribbons. Ab initio density functional calculations show that the armchair edge is lower in energy than the zigzag edge by 0.43 eV/angstrom. Both types of the edges are under the compressive stress. The zigzag edges are mechanically more stable than the armchair edges. Based on the calculated edge energies, the equilibrium shape of the BN flakes are found to be regular hexagonal, and dominated by the armchair edges. The zigzag ribbons are found to be half-metallic, whereas the armchair ribbons are semiconducting.