Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Introduction to Algorithms
Data Squashing by Empirical Likelihood
Data Mining and Knowledge Discovery
Active Learning to Recognize Multiple Types of Plankton
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Fast support vector machines for continuous data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
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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.