In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...
Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to...
Anastasios Gounaris, Rizos Sakellariou, Norman W. ...
This paper presents a lexical post-processing optimization for handwritten word recognition. The aim of this work is to explore the combination of different lexical postprocessing...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...