Some comments of Wolfe's `away step'
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
Core Vector Machines: Fast SVM Training on Very Large Data Sets
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
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
The Journal of Machine Learning Research
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids
Discrete Applied Mathematics
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Optimization Methods & Software
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
Maximum margin coresets for active and noise tolerant learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Two Algorithms for the Minimum Enclosing Ball Problem
SIAM Journal on Optimization
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
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We present a simple first-order approximation algorithm for the support vector classification problem. Given a pair of linearly separable data sets and ε ∈ (0,1), the proposed algorithm computes a separating hyperplane whose margin is within a factor of (1-ε) of that of the maximum-margin separating hyperplane. We discuss how our algorithm can be extended to nonlinearly separable and inseparable data sets. The running time of our algorithm is linear in the number of data points and in 1/ε. In particular, the number of support vectors computed by the algorithm is bounded above by O(ζ/ε) for all sufficiently small ε 0, where ζ is the square of the ratio of the distances between the farthest and closest pairs of points in the two data sets. Furthermore, we establish that our algorithm exhibits linear convergence. Our computational experiments, presented in the online supplement, reveal that the proposed algorithm performs quite well on standard data sets in comparison with other first-order algorithms. We adopt the real number model of computation in our analysis.