This paper extends the domain theoretic method for solving initial value problems, described in [8], to unbounded vector fields. Based on a sequence of approximations of the vecto...
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
This paper presents a novel basis function, called spherical piecewise constant basis function (SPCBF), for precomputed radiance transfer. SPCBFs have several desirable properties:...
Kun Xu, Yun-Tao Jia, Hongbo Fu, Shi-Min Hu, Chiew-...
— A subspace supervised learning algorithm named Discriminant Non-negative Matrix Factorization (DNMF) has been recently proposed for classifying human facial expressions. It dec...