A Comparison of Linear Support Vector Machine Algorithms on Large Non-Sparse Datasets

Abstract

This paper demonstrates the effectiveness of Linear Support Vector Machines (SVM) when applied to non-sparse datasets with a large number of instances. Two linear SVM algorithms are compared. The coordinate descent method (LibLinear) trains a linear SVM with the L2-loss function versus the cutting-plane algorithm (SVMperf), which uses a L1-loss function. Four Geographical Information System (GIS) datasets with over a million instances were used for this study. Each dataset consists of seven independent variables and a class label which denotes the urban areas versus the rural areas.