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Modern machine learning systems need to be able to efficiently process big data. Extracting useful patterns from massive collection of objects requires not only accurate, but also fast algorithms with limited computational complexity. However, one should remember that the problem with massive datasets lies not only in their volume. There is a number of difficulties embedded in the nature of data,...
Big data analytics, especially data stream mining, is among the most popular contemporary machine learning problems. More and more often real-life tasks could generate massive and continuous amounts of data. Standard classifiers cannot cope with a large volume of the training set and/or changing nature of the environment. In this paper, we deal with a problem of continuously arriving objects, that...
Modern computer systems generate massive amounts of data in real-time. We have come to the age of big data, where the amount of information exceeds the perceptive abilities of any human being. Frequently the massive data collections arrive over time, in the form of a data stream. Not only the volume and velocity of data poses a challenge for machine learning systems, but also its variability. Such...
One of the most important challenges for machine learning community is to develop efficient classifiers which are able to cope with data streams, especially with the presence of the so-called concept drift. This phenomenon is responsible for the change of classification task characteristics, and poses a challenge for the learning model to adapt itself to the current state of the environment. So there...
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