Bit Reduction Support Vector Machine Lawrence Hall, Tong Luo, Dmitry Goldgof, and Andrew Remsen   View Paper (PDF)   Return to Session 9: Preprocessing Techniques and Feature Selection Abstract Support vector machines are very accurate classifiers and have been widely used in many applications. However, the training and to a lesser extent prediction time of support vector machines on very large data sets can be very long. This paper presents a fast compression method to scale up support vector machines to large data sets. A simple bit reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop support vector machines which may be trained on weighted data. Experiments indicate that the bit reduction support vector machine produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to be more accurate than random sampling, when the data is not over-compressed. ©2006 Association for Computing Machinery