Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size inc...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...
We introduce an alternative to the smoothing technique approach for constrained optimization. As it turns out for any given smoothing function there exists a modification with part...
: Parallel iterative methods are powerful tool for solving large system of linear equations (LEs). The existing parallel computing research results are focussed mainly on sparse sy...
We have described previously a method of automatically constructing statistical models of shape. The method treats model-building as an optimisation problem by re-parameterising ea...
Rhodri H. Davies, Carole J. Twining, Christopher J...