DYNG: Dynamic Online Growing Neural Gas for stream data classification
نویسندگان
چکیده
In this paper we introduce Dynamic Online Growing Neural Gas (DYNG), a novel online stream data classification approach based on Online Growing Neural Gas (OGNG). DYNG exploits labelled data during processing to adapt the network structure as well as the speed of growth of the network to the requirements of the classification task. It thus speeds up learning for new classes/labels and dampens growth of the subnetwork representing the class once the class error converges. We show that this strategy is beneficial in life-long learning settings involving non-stationary data, giving DYNG an increased performance in highly non-stationary phases compared to OGNG.
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تاریخ انتشار 2013