We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
Software systems are designed and engineered to process data. However, software is data too. The size and variety of today's software artifacts and the multitude of stakehold...
Determining the time and means by which to travel from location A to location B for a person utilising a MANET requires the dissemination of both realtime and historic data. In thi...
Recently, the covariance region descriptor [1] has been proved robust and versatile for a modest computational cost. It enables efficient fusion of different types of features. Ba...