We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
Previous studies of end-user programmers have indicated a reliance on related examples for learning. Accordingly, we analyzed the projects contained in an online community with re...
While synaptic learning mechanisms have always been a core topic of neural computation research, there has been relatively little work on intrinsic learning processes, which change...
Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of softwar...
Abstract. This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian Dirichlet-Multinomial allocation (VBDM...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...