Decomposition methods for linear support vector machines
Neural Computation
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
A comparison of reduced support vector machines
International Journal of Intelligent Systems Technologies and Applications
Recognition of degraded characters using dynamic Bayesian networks
Pattern Recognition
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Cascade RSVM in Peer-to-Peer Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Structure Automatic Change in Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Recursive reduced least squares support vector regression
Pattern Recognition
Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration
Fundamenta Informaticae
A Robust Support Vector Regression Based on Fuzzy Clustering
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Selecting Samples and Features for SVM Based on Neighborhood Model
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
SVM Based Decision Analysis and Its Granular-Based Solving
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Communication-Efficient Classification in P2P Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A Fast Support Vector Machine Classification Algorithm Based on Karush-Kuhn-Tucker Conditions
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Prune support vector machines by an iterative process
International Journal of Computers and Applications
Subset based least squares subspace regression in RKHS
Neurocomputing
Selection of basis functions guided by the L2 soft margin
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The minimum redundancy-maximum relevance approach to building sparse support vector machines
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Efficient reduction of support vectors in kernel-based methods
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A maximizing-discriminability-based self-organizing fuzzy network for classification problems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Privacy-preserving outsourcing support vector machines with random transformation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
On Learning and Cross-Validation with Decomposed Nyström Approximation of Kernel Matrix
Neural Processing Letters
Application of SVM-based filter using LMS learning algorithm for image denoising
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Simplifying SVM with weighted LVQ algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A speedup method for SVM decision
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Probability estimation in error correcting output coding framework using game theory
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Cooperative clustering for training SVMs
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Simplify decision function of reduced support vector machines
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Satrap: data and network heterogeneity aware P2P data-mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
ASVMFC: adaptive support vector machine based fuzzy classifier
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration
Fundamenta Informaticae
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
Twin least squares support vector regression
Neurocomputing
Classification in P2P networks with cascade support vector machines
ACM Transactions on Knowledge Discovery from Data (TKDD)
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
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Recently the reduced support vector machine (RSVM) was proposed as an alternate of the standard SVM. Motivated by resolving the difficulty on handling large data sets using SVM with nonlinear kernels, it preselects a subset of data as support vectors and solves a smaller optimization problem. However, several issues of its practical use have not been fully discussed yet. For example, we do not know if it possesses comparable generalization ability as the standard SVM. In addition, we would like to see for how large problems RSVM outperforms SVM on training time. In this paper we show that the RSVM formulation is already in a form of linear SVM and discuss four RSVM implementations. Experiments indicate that in general the test accuracy of RSVM are a little lower than that of the standard SVM. In addition, for problems with up to tens of thousands of data, if the percentage of support vectors is not high, existing implementations for SVM is quite competitive on the training time. Thus, from this empirical study, RSVM will be mainly useful for either larger problems or those with many support vectors. Experiments in this paper also serve as comparisons of: 1) different implementations for linear SVM and 2) standard SVM using linear and quadratic cost functions.