Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Linear Programming in Linear Time When the Dimension Is Fixed
Journal of the ACM (JACM)
Alpha seeding for support vector machines
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Efficient Computation and Model Selection for the Support Vector Regression
Neural Computation
A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
IEEE Transactions on Neural Networks
A New Solution Path Algorithm in Support Vector Regression
IEEE Transactions on Neural Networks
Signal Processing
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This paper describes an improved algorithm for the numerical solution to the support vector machine (SVM) classification problem for all values of the regularization parameter C. The algorithm is motivated by the work of Hastie et al. and follows the main idea of tracking the optimality conditions of the SVM solution for ascending value of C. It differs from Hastie's approach in that the tracked path is not assumed to be 1-D. Instead, a multidimensional feasible space for the optimality condition is used to solve the tracking problem. Such a treatment allows the algorithm to properly handle data sets which Hastie's approach fails. These data sets are characterized by the presence of linearly dependent points (in the kernel space), duplicate points, or nearly duplicate points. Such data sets are quite common among many real-world data, especially those with nominal features. Other contributions of this paper include a unifying formulation of the tracking process in the form of a linear programming problem, update formula for the linear programs, considerations that guard against accumulation of errors resulting from the use of incremental updates, and routines to speed up the algorithm. The algorithm is implemented under the Matlab environment and is available for download. Experiments with several data sets including data set having up to several thousand data points are reported.