Machine Learning
Making large-scale support vector machine learning practical
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
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
Training a Support Vector Machine in the Primal
Neural Computation
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
Trust region Newton methods for large-scale logistic regression
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
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
Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Fast BMU Search for Support Vector Machine
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
The Journal of Machine Learning Research
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
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
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
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
A new framework for dissimilarity and similarity learning
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Optimized online rank learning for machine translation
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Measuring the Visual Complexities of Web Pages
ACM Transactions on the Web (TWEB)
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
Smoothing multivariate performance measures
The Journal of Machine Learning Research
Fast linearization of tree kernels over large-scale data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Line filtering for surgical tool localization in 3D ultrasound images
Computers in Biology and Medicine
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We have developed a new Linear Support Vector Machine (SVM) training algorithm called OCAS. Its computational effort scales linearly with the sample size. In an extensive empirical evaluation OCAS significantly outperforms current state of the art SVM solvers, like SVMlight, SVMperf and BMRM, achieving speedups of over 1,000 on some datasets over SVMlight and 20 over SVMperf, while obtaining the same precise Support Vector solution. OCAS even in the early optimization steps shows often faster convergence than the so far in this domain prevailing approximative methods SGD and Pegasos. Effectively parallelizing OCAS we were able to train on a dataset of size 15 million examples (itself about 32GB in size) in just 671 seconds --- a competing string kernel SVM required 97,484 seconds to train on 10 million examples sub-sampled from this dataset.