[Show abstract][Hide abstract]ABSTRACT: Support vector regression (SVR) and support vector classification (SVC) are popular learning techniques, but their use with kernels is often time consuming. Recently, linear SVC without kernels has been shown to give competitive accuracy for some applications, but enjoys much faster training/ testing. However, few studies have focused on linear SVR. In this paper, we extend state-of-theart training methods for linear SVC to linear SVR. We show that the extension is straightforward for some methods, but is not trivial for some others. Our experiments demonstrate that for some problems, the proposed linear-SVR training methods can very efficiently produce models that are as good as kernel SVR.
Article · Nov 2012 · Journal of Machine Learning Research
[Show abstract][Hide abstract]ABSTRACT: GLMNET proposed by Friedman et al. is an algorithm for generalized linear models with elastic net. It has been widely applied to solve L1-regularized logistic regression. However, recent experiments indicated that the existing GLMNET implementation may not be stable for large-scale problems. In this paper, we propose an improved GLMNET to address some theoretical and implementation issues. In particular, as a Newton-type method, GLMNET achieves fast local convergence, but may fail to quickly obtain a useful solution. By a careful design to adjust the effort for each iteration, our method is efficient regardless of loosely or strictly solving the optimization problem. Experiments demonstrate that the improved GLMNET is more efficient than a state-of-the-art coordinate descent method.
[Show abstract][Hide abstract]ABSTRACT: In this paper, we decompose the problem of active learning into two parts, learning with few examples and learning by querying labels of samples. The first part is achieved mainly by SVM classifiers. We also consider variants based on transductive learning. In the second part, based on SVM decision values, we propose a framework to flexibly select points for query. Our experiments are conducted on the data sets of Causality Active Learning Challenge. With measurements of Area Under Curve (AUC) and Area under the Learning Curve (ALC), we find suitable methods for different data sets.