Articles liés à Improvising Learning Imbalanced Data in Data Streams

Improvising Learning Imbalanced Data in Data Streams - Couverture souple

Bhowmick, Kiran

 
9798889951308: Improvising Learning Imbalanced Data in Data Streams

Synopsis

Data streams are an unbounded sequence of data records, whose entry rate is usually high and whose dispersions frequently change. Algorithms used for analyzing data streams must be able to process data in a very limited time frame and memory. This

is due to the fact that, unlike traditional data, entire data streams cannot be stored in the memory .

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