Making large-scale support vector machine learning practical
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
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
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse kernel SVMs via cutting-plane training
Machine Learning
Guest editors' introduction: special issue of selected papers from ECML PKDD 2009
Data Mining and Knowledge Discovery
Sparse kernel SVMs via cutting-plane training
Machine Learning
Guest editors' introduction: Special Issue from ECML PKDD 2009
Machine Learning
Sparse Kernel SVMs via Cutting-Plane Training
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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
Example-dependent basis vector selection for kernel-based classifiers
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Analog Integrated Circuits and Signal Processing
Training linear ranking SVMs in linearithmic time using red-black trees
Pattern Recognition Letters
Fast support vector machines for structural Kernels
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Algorithms and Applications
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
Integrating cue descriptors in bubble space for place recognition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
| Hi-index | 0.00 |
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. This results in two benefits. First, the added flexibility makes it possible to find sparser solutions of good quality, substantially speeding-up prediction. Second, the improved sparsity can also make training of Kernel SVMs more efficient, especially for high-dimensional and sparse data (e.g. text classification). This has the potential to make training of Kernel SVMs tractable for large training sets, where conventional methods scale quadratically due to the linear growth of the number of SVs. In addition to a theoretical analysis of the algorithm, we also present an empirical evaluation.