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Chapter 3 SELECTING DATA FOR FAST SUPPORT VECTOR MACHINE TRAINING Jigang Wang, Predrag Neskovic, Leon N Cooper Institute for Brain and Neural Systems and Department of Physics Brown University, Providence RI 02912, USA jigang@, pedja@, Leon Cooper@ Abstract In recent years, support vector machines (SVMs) have become a popu- lar tool for pattern recognition and machine learning. Training a SVM involves solving a constrained quadratic programming problem, which requires large memory and enormous amounts of training time for large- scale problems. In contrast, the SVM decision function is fully deter- mined by a small subset of the training data, called support vectors. Therefore, it is desirable to remove from the training set data that are irrelevant to the final decision function. In this work we propose two new methods that select a subset of data for SVM training. Using real-world datasets, we compare the effectiveness of the proposed data selection strategies in terms of their ability to reduce the training set size while maintaining the generalization performance of the resulting SVM classifiers. Our experimental results show that a significant amount of training data can be removed by our proposed methods without degrad- ing the performance of the resulting SVM classifiers. Keywords: Support vector machines, quadratic programming, data selection, sta- tistical confidence, Hausdorff distance, random sampling 1. Introduction Support vector machines (SVMs), introduced by Vapnik and cowork- ers in the structural risk minimization (SRM) framework [1–3], have gained wi