Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven t...
In this paper we introduce a new sparseness inducing prior which does not involve any (hyper)parameters that need to be adjusted or estimated. Although other applications are poss...
Double JPEG compression detection is of significance in digital forensics. We propose an effective machine learning based scheme to distinguish between double and single JPEG comp...
In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the cons...
We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers...