Online real-time learning strategies for data streams for Neurocomputing

Learning from on-line data streams is a research area of growing interest, because large volumes of data are continuously generated from multi-scale sensor networks, production and manufacturing lines, social media, the Internet, wireless communications etc., often with a high incoming rate. This desires the usage of real time learning and modelling algorithms. An important aspect in data stream mining is that the data analysis system, the learner, has no control over the order of samples that arrive over time --they simply arrive in the same order they are acquired and recorded. Also, the learning algorithms usually have to be fast enough in order to cope with (near) real-time and on-line demands. This usually requires a single-pass learning procedure, restricting the algorithm to update models and statistical information in a sample-wise manner, without using any prior data.