Fast support vector machines for continuous data

Support vector machines can be trained to be very accurate classifiers and have been 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 trained on the 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, typically, be more accurate than random sampling when the data are not over-compressed.