OLLAWV: OnLine Learning Algorithm using Worst-Violators

Abstract

Due to the ever-growing nature of dataset sizes, the need for scalable and accurate machine learning algorithms has become evident. Stochastic gradient descent methods are popular tools used to optimize large-scale learning problems because of their generalization performance, simplicity, and scalability. This paper proposes a novel stochastic, also known as online, learning algorithm for solving the L1 support vector machine (SVM) problem, named OnLine Learning Algorithm using Worst-Violators (OLLAWV). Unlike other stochastic methods, OLLAWV eliminates the need for specifying the maximum number of iterations and the use of a regularization term. OLLAWV uses early stopping for controlling the size of the margin instead of the regularization term. The experimental study, performed under very strict nested cross-validation (a.k.a., double resampling), evaluates and compares the performance of this proposal with state-of-the-art SVM kernel methods that have been shown to outperform traditional and widely used approaches for solving L1-SVMs such as Sequential Minimal Optimization. OLLAWV is also compared to 5 other traditional non-SVM algorithms. The results over 23 datasets show OLLAWV's superior performance in terms of accuracy, scalability, and model sparseness, making it suitable for large-scale learning.