Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Stabilization of Co-3 - Oxoclusters in a pcu Net: Synthesis, Structure, Solvent Exchange (Single Crystal to Single Crystal) and Magnetic Studies

The solvothermal reaction of CoCl(2)4H(2)O and 4,4-sulfonyldibenzoic acid (H(2)SDBA) resulted in the formation of a three-dimensional coordination polymer Co-3(C14H8O6S)(3)(DMA)(2)(MeOH)].DMA (Ia) consisting of trinuclear Co-3 oxo-cluster units. The Co-3 trimeric units are connected by SDBA(2-) anions leading to a three dimensional structure with a pcu topology. The terminal methanol molecules could be exchanged in a single crystal to single crystal (SCSC) fashion by other similar solvent molecules (ethanol, acetonitrile, water, ethyleneglycol). Magnetic studies on the parent compound, Ia, indicate antiferromagnetic interactions between the central metal atoms.