Published July 2022
| Published + Submitted
Conference Paper
Open
Closure Operators: Complexity and Applications to Classification and Decision-making
- Creators
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Hamze Bajgiran, Hamed
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Echenique, Federico
Chicago
Abstract
We study the complexity of closure operators, with applications to machine learning and decision theory. In machine learning, closure operators emerge naturally in data classification and clustering. In decision theory, they can model equivalence of choice menus, and therefore situations with a preference for flexibility. Our contribution is to formulate a notion of complexity of closure operators, which translate into the complexity of a classifier in ML, or of a utility function in decision theory.
Additional Information
© 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. The first author gratefully acknowledges support from Beyond Limits (Learning Optimal Models) through CAST (The Caltech Center for Autonomous Systems and Technologies). The second author gratefully acknowledges support from the National Science Foundation under grant number SES-1558757. We thank Chris Chambers for many comments and suggestions. We would like to thank our reviewers for their constructive feedback.Attached Files
Published - 3490486.3538253.pdf
Submitted - 2202.05339.pdf
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3490486.3538253.pdf
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Additional details
- Eprint ID
- 115375
- Resolver ID
- CaltechAUTHORS:20220707-170604478
- Beyond Limits
- NSF
- SES-1558757
- Created
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2022-07-08Created from EPrint's datestamp field
- Updated
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2022-07-29Created from EPrint's last_modified field
- Caltech groups
- Center for Autonomous Systems and Technologies (CAST)