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Incremental learning from stream data

Recent years have witnessed major increasing interest in the topic of incremental learning under scenarios of large amount of continuous data flow. The big challenge in the community is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision making processes. This international collaborative research with China develops a general adaptive incremental learning framework that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance, as confirmed by simulation results over real-world data sets.