We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
In the inductive inference framework of learning in the limit, a variation of the bounded example memory (Bem) language learning model is considered. Intuitively, the new model co...
Sanjay Jain, Steffen Lange, Samuel E. Moelius, San...
We investigate the runtime of a Binary Particle Swarm Optimizer (PSO) for optimizing pseudo-Boolean functions f : {0, 1}n → R. The Binary PSO maintains a swarm of particles sear...
Robustness, the ability to analyze any input regardless of its grammaticality, is a desirable property for any system dealing with unrestricted natural language text. Error-repair...
We define a subclass of timed automata, called oscillator timed automata, suitable to model biological oscillators. Coupled biological oscillators may synchronise, as emerging be...
Iterative Compression has recently led to a number of breakthroughs in parameterized complexity. Here, we show that the technique can also be useful in the design of exact exponen...
Fedor V. Fomin, Serge Gaspers, Dieter Kratsch, Mat...
We define a program semantics that is preserved by dependence-based slicing algorithms. It is a natural extension, to non-terminating programs, of the semantics introduced by Wei...
Richard W. Barraclough, David Binkley, Sebastian D...