Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The Journal of Machine Learning Research
Nonconvex Online Support Vector Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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JKernelMachines is a Java library for learning with kernels. It is primarily designed to deal with custom kernels that are not easily found in standard libraries, such as kernels on structured data. These types of kernels are often used in computer vision or bioinformatics applications. We provide several kernels leading to state of the art classification performances in computer vision, as well as various kernels on sets. The main focus of the library is to be easily extended with new kernels. Standard SVM optimization algorithms are available, but also more sophisticated learning-based kernel combination methods such as Multiple Kernel Learning (MKL), and a recently published algorithm to learn powered products of similarities (Product Kernel Learning).