The TREC-8 ltering track measures the ability of systems to build persistent user pro les which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive ltering, batch ltering, and routing. In adaptive ltering, the system begins with only a topic statement and must learn a better pro le from on-line feedback. Batch ltering and routing are more tra...

It is well known, that online neural gas (NG) possesses a magnification exponent different from the information theoretically optimum one in adaptive map formation. The exponent can explicitely be controlled by a small change of the learning algorithm. Batch NG constitutes a fast alternative optimization scheme for NG vector quantizers which possesses the same magnification factor as standard o...

digital libraries offer opportunities for e-learning that are not possible in their physical counterparts. digital libraries complement other learning environments, such as those provided in distance education and courses offered online. like e-learning environments, they provide flexibility of time and place. digital libraries have the potential to offer unprecedented resources to support e-le...

Batch reinforcement learning is a subfield of dynamic programming-based reinforcement learning. Originally defined as the task of learning the best possible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the effic...

Variational methods provide a computationally scalable alternative to Monte Carlo methods for large-scale, Bayesian nonparametric learning. In practice, however, conventional batch and online variational methods quickly become trapped in local optima. In this paper, we consider a nonparametric topic model based on the hierarchical Dirichlet process (HDP), and develop a novel online variational ...