James: Unlocking the Power of Streaming Data: Online Learning for Diverse Applications, Kartoniert / Broschiert

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Data streams are de ned as large sequences of data, gathered from sources such as sensor networks and customer click streams, that are possibly in nite and temporarily ordered [7, 22]. Instances in data streams arrive fast, either in batches of data, or instance-by-instance; each instance needs to be processed in a timely manner. Due to these characteristics, such as large amount of data and time constraints, tra-ditional static machine learning algorithms are unsuitable for direct use [7]. That is, techniques learning from data streams need to maintain their performance throughout the stream while limiting memory and processing time. Moreover, evolving or non-stationary data streams are susceptible to changes in the distribution of data, also known as concept drifts.