We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalizatio...
The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a l...
Co-evolution has been receiving increased attention as a method for multi agent simultaneous learning. This paper discusses how multiple robots can emerge cooperative behaviors thr...
Abstract. E cient data mining algorithms are crucial fore ective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a ...
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...