This paper introduces an architectural style for enabling end-users to quickly design and deploy software systems in domains characterized by highly personalized and dynamic requi...
As standard volume rendering is based on an integral in physical space (or “coordinate space”), it is inherently dependent on the scaling of this space. Although this dependen...
In order to overcome the computation and storage problem for large-scale data set, an efficient iterative method of Generalized Discriminant Analysis is proposed. Because sample v...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...