The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
The analysis of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Errata to "On the convergence of the decomposition method for support vector machines"
IEEE Transactions on Neural Networks
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Working Set Selection Using Second Order Information for Training Support Vector Machines
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
An algorithm to cluster data for efficient classification of support vector machines
Expert Systems with Applications: An International Journal
Choosing the Kernel Parameters for the Directed Acyclic Graph Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
On the Equivalence of the SMO and MDM Algorithms for SVM Training
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A convergent hybrid decomposition algorithm model for SVM training
IEEE Transactions on Neural Networks
Incremental clustering in geography and optimization spaces
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
Computational Optimization and Applications
Radial kernels and their reproducing kernel Hilbert spaces
Journal of Complexity
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
General polynomial time decomposition algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Efficient astronomical data classification on large-scale distributed systems
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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This paper studies the convergence properties of a general class of decomposition algorithms for support vector machines (SVMs). We provide a model algorithm for decomposition, and prove necessary and sufficient conditions for stepwise improvement of this algorithm. We introduce a simple “rate certifying” condition and prove a polynomial-time bound on the rate of convergence of the model algorithm when it satisfies this condition. Although it is not clear that existing SVM algorithms satisfy this condition, we provide a version of the model algorithm that does. For this algorithm we show that when the slack multiplier C satisfies \sqrt{1/2} ≤ C ≤ mL, where m is the number of samples and L is a matrix norm, then it takes no more than 4LC2m4/ε iterations to drive the criterion to within ε of its optimum.