We study a refined framwork of parameterized complexity theory where the parameter dependendence of fixed-parameter tractable algorithms is not arbitrary, but restricted by a fu...
Then these analytically motivated abstractions were gradually made more intricate as the body of mathematical techniques grew. The trend went from elementary analysis of complex, i...
We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the...
Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint ...
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the di...
During its formative decades the software community looked twice to the theories of ChristopherAlexander for inspiration, both times failing to completely master the architect’s...