The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A parallel mixture of SVMs for very large scale problems
Neural Computation
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
A fast SVM training method for very large datasets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Support Vector Machine (SVM) has been successful in multiple areas and is widely accepted as the best off the shelf algorithm for classification. A standard SVM has O(n3) time and O(n3) space complexities, hence making it limited in its usability for large database. We know that in real world scenario, most of the databases where Data Mining is used are large. This paper reviews various algorithms and techniques that have been brought forth since 1995 by researchers for implementing SVMs in a practical manner for large databases.