Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Matrix computations (3rd ed.)
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
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
SIAM Journal on Optimization
Primal--Dual Path-Following Algorithms for Semidefinite Programming
SIAM Journal on Optimization
On a commutative class of search directions for linear programming over symmetric cones
Journal of Optimization Theory and Applications
A Simple Decomposition Method for Support Vector Machines
Machine Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
A large-update primal-dual interior-point method for second-order cone programming
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
In this paper we propose a new fast learning algorithm for the support vector machine (SVM). The proposed method is based on the technique of second-order cone programming. We reformulate the SVM's quadratic programming problem into the second-order cone programming problem. The proposed method needs to decompose the kernel matrix of SVM's optimization problem, and the decomposed matrix is used in the new optimization problem. Since the kernel matrix is positive semidefinite, the dimension of the decomposed matrix can be reduced by decomposition (factorization) methods. The performance of the proposed method depends on the dimension of the decomposed matrix. Experimental results show that the proposed method is much faster than the quadratic programming solver LOQO if the dimension of the decomposed matrix is small enough compared to that of the kernel matrix. The proposed method is also faster than the method proposed in (S. Fine and K. Scheinberg, 2001) for both low-rank and full-rank kernel matrices. The working set selection is an important issue in the SVM decomposition (chunking) method. We also modify Hsu and Lin's working set selection approach to deal with large working set. The proposed approach leads to faster convergence.