Abstract
In online learning, the concept drifts refers to the situations where the objective variable conforming to the input data changes over time. This change in distribution of data over time can be studied in different forms namely abrupt, gradual, incremental and reoccurring concepts. This paper illustrates the incremental learning with concept drift, different forms of drift, causes of concept drift and present a set of illustrative concept drift applications related with real-life problems. The various dimensions such as learning speed, prediction and classification accuracy, mistakes penalty, adversary activities and true labels are also discussed in relevance to these applications having the problem of concept drift.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
