Making large-scale support vector machine learning practical
Advances in kernel methods
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Efficient svm training using low-rank kernel representations
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
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A statistical framework for genomic data fusion
Bioinformatics
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Cutting-plane training of structural SVMs
Machine Learning
Robust Stochastic Approximation Approach to Stochastic Programming
SIAM Journal on Optimization
Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Introduction to data mining for sustainability
Data Mining and Knowledge Discovery
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Sensor measurements from diverse locations connected with possibly low bandwidth communication channels pose a challenge of resource-restricted distributed data analyses. In such settings it would be desirable to perform learning in each location as much as possible, without transferring all data to a central node. Applying the support vector machines (SVMs) with nonlinear kernels becomes nontrivial, however. In this paper, we present an efficient optimization scheme for training SVMs over such sensor networks. Our framework performs optimization independently in each node, using only the local features stored in the respective node. We make use of multiple local kernels and explicit approximations to the feature mappings induced by them. Together they allow us constructing a separable surrogate objective that provides an upper bound of the primal SVM objective. A central coordination is also designed to adjust the weights among local kernels for improved prediction, while minimizing communication cost.