Making large-scale support vector machine learning practical
Advances in kernel methods
Training Invariant Support Vector Machines
Machine Learning
Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Training a Support Vector Machine in the Primal
Neural Computation
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Second-order smo improves svm online and active learning
Neural Computation
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Better multiclass classification via a margin-optimized single binary problem
Pattern Recognition Letters
Coefficient Structure of Kernel Perceptrons and Support Vector Reduction
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Classification of Protein Interaction Sentences via Gaussian Processes
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Sparse kernel SVMs via cutting-plane training
Machine Learning
Cutting-plane training of structural SVMs
Machine Learning
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
On-line independent support vector machines
Pattern Recognition
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Selecting a reduced set for building sparse support vector regression in the primal
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Classifier complexity reduction by support vector pruning in kernel matrix learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Greedy-based design of sparse two-stage SVMs for fast classification
Proceedings of the 29th DAGM conference on Pattern recognition
Selection of basis functions guided by the L2 soft margin
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Sparse least squares support vector regressors trained in the reduced empirical feature space
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 learning and feature selection in high-dimensional regression
Neural Computation
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
An effective method of pruning support vector machine classifiers
IEEE Transactions on Neural Networks
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Large-scale support vector learning with structural kernels
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
New separating hyperplane method with application to the optimisation of direct marketing campaigns
Pattern Recognition Letters
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation
International Journal of Data Mining and Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
Exploiting separability in large-scale linear support vector machine training
Computational Optimization and Applications
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
Using the leader algorithm with support vector machines for large data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Building sparse support vector machines for multi-instance classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Hierarchical linear support vector machine
Pattern Recognition
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Hybrid classifiers for object classification with a rich background
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
Multi-Task learning using shared and task specific information
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Learning sparse kernel classifiers in the primal
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Estimation of autocorrelation space for classification of bio-medical signals
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
The Journal of Machine Learning Research
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
Hi-index | 0.00 |
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.