SMO-based pruning methods for sparse least squares support vector machines

Solutions of least squares support vector machines (LS-SVMs) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS-SVM. In this paper, we propose a new pruning algorithm for sparse LS-SVMs: the sequential minimal optimization (SMO) method is introduced into pruning process; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments

Baicalin Normalizes Blood Glucose Level in Streptozotocin -induced Diabetic Rats

This study aimed to determine the effect of baicalin on insulin resistance, glucose absorption, and blood lipids in type 2 diabetic rat model. Diabetic rats were treated with baicalin (40, 80 mg/kg) for 40 days. The results showed that diabetic rats treated with baicalin resulted in a significant decrease in the concentration of plasma triglycerides and high-density lipoprotein cholesterol, improved the body weight. Furthermore, baicalin markedly decreased blood glucose level in the diabetic rats. The levels of plasma insulin and resistin exhibited significantly lower in the diabetic rats treated with baicalin than those of the model group. These findings suggest that baicalin can improve adipose metabolic disturbance in the experimental type 2 diabetic rats, can effectively ameliorate insulin resistance and plasma glucose transport by decreasing the levels of plasma resistin.Colegio de Farmacéuticos de la Provincia de Buenos Aire