Batch-incremental versus instance-incremental learning in dynamic and evolving data

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Title

Batch-incremental versus instance-incremental learning in dynamic and evolving data

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically- infinite stream of examples using limited time and memory, while being able to predict at any point. Two approaches dominate the literature: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these...

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Format

Research paper

Research format

Conference item

Date created

2012

Creator

Read, Jesse / Bifet, Albert / Pfahringer, Bernhard / Holmes, Geoffrey

URL

https://hdl.handle.net/10289/6853

Related subjects

data streams / dynamic / evolving / incremental / on-line / Machine learning

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