Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the...
suitable disk layout and network transmission schedule to minimize allocated resources (buffer size, bandwidth, ..., etc.) with maximum resource utilization. In this paper, the rea...
Grid Workflows are emerging as practical programming models for solving large e-scientific problems on the Grid. However, it is typically assumed that the workflow components eith...
We propose a generalized model with configurable discretizer actuators as a solution to the problem of the discretization of massive numerical datasets. Our solution is based on a ...