An Efficient v-Minimum Absolute Deviation Distribution Regression Machine

Abstract

Support Vector Regression (SVR) and its variants are widely used regression algorithms, and they have demonstrated high generalization ability. This research proposes a new SVR-based regressor : v-minimum absolute deviation distribution regression (v-MADR) machine. Instead of merely minimizing structural risk, as with v-SVR, v-MADR aims to achieve better generalization performance by minimizing both the absolute regression deviation mean and the absolute regression deviation variance, which takes into account the positive and negative values of the regression deviation of sample points. For optimization, we propose a dual coordinate descent (DCD) algorithm for small sample problems, and we also propose an averaged stochastic gradient descent (ASGD) algorithm for large-scale problems. Furthermore, we study the statistical property of v-MADR that leads to a bound on the expectation of error. The experimental results on both artificial and real datasets indicate that our v-MADR has significant improvement in generalization performance with less training time compared to the widely used v-SVR, LS-SVR, ε-TSVR, and linear ε-SVR. Finally, we open source the code of v-MADR at https://github.com/AsunaYY/v-MADR for wider dissemination. INDEX TERMS v-support vector regression, absolute regression deviation mean, absolute regression deviation variance, dual coordinate descent algorithm.

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

“…In this function, it is permissible to differ from the expected value (loss function). The ideal solution is then identified by applying structural risk reduction concepts to the loss-function-measured risk [26,27].…”

“…So far, various kernel functions have been introduced, among which they can be classified as polynomial kernels. The radial basis function has been pointed out [25,26].…”