A note on the decomposition methods for support vector regression
Neural Computation
Training v-support vector regression: theory and algorithms
Neural Computation
A Random Sampling Technique for Training Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
Parameter detection of thin films from their x-ray reflectivity by support vector machines
Applied Numerical Mathematics
A tutorial on support vector regression
Statistics and Computing
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Efficient optimization of support vector machine learning parameters for unbalanced datasets
Journal of Computational and Applied Mathematics
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
General Polynomial Time Decomposition Algorithms
The Journal of Machine Learning Research
On the complexity of working set selection
Theoretical Computer Science
Semismooth Newton support vector machine
Pattern Recognition Letters
A convergent decomposition algorithm for support vector machines
Computational Optimization and Applications
Quadratic programming formulations for classificationand regression
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
Candidate working set strategy based SMO algorithm in support vector machine
Information Processing and Management: an International Journal
A convergent hybrid decomposition algorithm model for SVM training
IEEE Transactions on Neural Networks
A Simple Proof of the Convergence of the SMO Algorithm for Linearly Separable Problems
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Gaps in support vector optimization
COLT'07 Proceedings of the 20th annual conference on Learning theory
Generalized SMO-style decomposition algorithms
COLT'07 Proceedings of the 20th annual conference on Learning theory
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Computational Optimization and Applications
Convergence of a new decomposition algorithm for support vector machines
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
A common framework for the convergence of the GSK, MDM and SMO algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
The Journal of Machine Learning Research
Training support vector machines via SMO-type decomposition methods
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Some improvements to a parallel decomposition technique for training support vector machines
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
An adaptive support vector machine learning algorithm for large classification problem
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
General polynomial time decomposition algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Fast training of SVM via morphological clustering for color image segmentation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Fast training of linear programming support vector machines using decomposition techniques
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
The Journal of Machine Learning Research
| Hi-index | 0.00 |
The decomposition method is currently one of the major methods for solving support vector machines (SVM). Its convergence properties have not been fully understood. The general asymptotic convergence was first proposed by Chang et al. However, their working set selection does not coincide with existing implementation. A later breakthrough by Keerthi and Gilbert (2000, 2002) proved the convergence finite termination for practical cases while the size of the working set is restricted to two. In this paper, we prove the asymptotic convergence of the algorithm used by the software SVMlight and other later implementation. The size of the working set can be any even number. Extensions to other SVM formulations are also discussed