Kernel-Machine Library 0.1

The Kernel-Machine Library is a freely available (released under the GPL) C++ library to promote the use and progress of kernel machines. It is both for academic use and for developing real world applications.
The Kernel-Machine Library draws heavily from features of modern C++ such as template meta-programming to achieve high performance while at the same time offering a comfortable interface.
It enables compile-time selection of specialised algorithms on the basis of data types: for example, the specific case of a SVM in combination with a linear kernel can be computed by a specialised efficient algorithm.
The Kernel-Machine Library has implementations for the following kernel machines and their cited algorithms:
- Support Vector Machine [1, 2, 3]
- Relevance Vector Machine [4]
- Kernel Recursive Least Squares [5]
- Adaptive Sparseness using Jeffreys Prior [6]
- Smooth Relevance Vector Machine [7]
Up till now, the focus has been on regression. The handling of classification and ranking problems is being added.