NDLI: Reinforcement learning for active model selection

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Content Provider	ACM Digital Library
Author	Greiner, Russell Kapoor, Aloak
Abstract	In many practical Machine Learning tasks, there are costs associated with acquiring the feature values of training instances, as well as a hard learning budget which limits the number of feature values that can be purchased. In this budgeted learning scenario, it is important to use an effective "data acquisition policy", that specifies how to spend the budget acquiring training data to produce an accurate classifier. This paper examines a simplified version of this problem, "active model selection" [10]. As this is a Markov decision problem, we consider applying reinforcement learning (RL) techniques to learn an effective spending policy. Despite extensive training, our experiments on various versions of the problem show that the performance of RL techniques is inferior to existing, simpler spending policies.
Starting Page	17
Ending Page	23
Page Count	7
File Format	PDF
ISBN	1595932089
DOI	10.1145/1089827.1089829
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Training costs Learning costs Data acquisition Budgeted learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Reinforcement learning for active model selection