Bit Reduction Support Vector Machine

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 trained on the weighted data. Experiments indicate that the bit reduction support vector machine produces a significant reduction of 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.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, Andrew Remsen
• Keywords: Bit Reduction, Data Mining, ICDM 2005, Large Data Sets, Support Vector Machine