Recently we presented a new approach [20] to the classification problem arising in data mining. It is based on the regularization network approach but in contrast to other methods...
We address the problem of computing an optimal value function for Markov decision processes. Since finding this function quickly and accurately requires substantial computation ef...
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a ...
For various 3D shape analysis tasks, the LaplaceBeltrami(LB) embedding has become increasingly popular as it enables the efficient comparison of shapes based on intrinsic geometry...
Rongjie Lai, Yonggang Shi, Kevin Scheibel, Scott F...
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...