Optimizing working sets for training support vector regressors by Newton's method

Abstract

In this paper, we train support vector regressors (SVRs) fusing sequential minimal optimization (SMO) and Newton's method. We use the SVR formulation that includes the absolute variables. A partial derivative of the absolute variable with respect to the associated variable is indefinite when the variable takes on zero. We determine the derivative value according to whether the optimal solution exits in the positive region, negative region, or at zero. In selecting working set, we use the method that we have developed for the SVM, namely, in addition to the pair of variables selected by SMO, loop variables that repeatedly appear in training, are added to the working set. By this method the working set size is automatically determined. We demonstrate the validity of our method over SMO using several benchmark data sets.

Improved Rate Control Algorithm Based on R- Model in High Efficiency Video Coding

“…Firstly, the algorithm guides the bits allocation in LCU layer by weighing the local kinematically spatial complexity and the temporal complexity of the global motion in this layer. Secondly, the video distortion and Newton method [4] are used to iteratively calculate the model parameters  and  of the rate control model. Experiment results indicates that the rate control algorithm in this paper can reduce the Bit-error and improve the video quality.…”