We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
In this paper, we examine an emerging variation of the classification problem, which is known as the inverse classification problem. In this problem, we determine the features to b...
Comparing humans and machines is one important source of information about both machine and human strengths and limitations. Most of these comparisons and competitions are performe...
Javier Insa-Cabrera, David L. Dowe, Sergio Espa&nt...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...