SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
The Journal of Machine Learning Research
Resilient approximation of kernel classifiers
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
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Computers and Graphics
Proceedings of the 50th Annual Design Automation Conference
DARWIN: a framework for machine learning and computer vision research and development
The Journal of Machine Learning Research
A study of application-level recovery methods for transient network faults
ScalA '13 Proceedings of the Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
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There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these tools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools.