A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a ...
In recent years, the evolution of artificial neural networks or neuroevolution has brought promising results in solving difficult reinforcement learning problems. But, like standa...
We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we ...
— To achieve seamless integration of the real scene with virtual objects, we need to obtain accurate illumination condition of the real scene. Using HDRI (High Dynamic Range Imag...
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...