Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from da...
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
In several information retrieval (IR) systems there is a possibility for user feedback. Many machine learning methods have been proposed that learn from the feedback information in...
In this paper, based on recent scientific findings in the fields of neurology, evolutionary psychology and cognitive psychology we propose a software architecture and technology su...
This paper proposes the use of constructive ordinals as mistake bounds in the on-line learning model. This approach elegantly generalizes the applicability of the on-line mistake ...