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A new technique for "hedging" predictions was presented and discussed
recently by Alexander Gammerman and Vladimir Vovk at a special meeting of the British Computer Society. The method can be applied to many algorithms, including Support Vector Machines, Kernel Ridge Regression,
Kernel Nearest Neighbours and other state-of-the-art methods.
The hedged predictions include confidence measures that are provably valid and it becomes possible to control the number of errors by selecting a suitable confidence level.
The discussants of the technique included Vladimir Vapnik, Alexey Chervonenkis, Glenn Shafer, Zhiyuan Luo and many others.
The paper and the discussion can be found here.