Keywords
machine learning, instance-based
Abstract
We consider learning in a transductive setting using instance-based learning (k-NN) and present a method for constructing a data-dependent distance “metric” using both labeled training data as well as available unlabeled data (that is to be classified by the model). This new data-driven measure of distance is empirically studied in the context of various instance-based models and is shown to reduce error (compared to traditional models) under certain learning conditions. Generalizations and improvements are suggested.
Original Publication Citation
Jared Lundell and Dan Ventura, "A Data-dependent Distance Measure for Transductive Instance-based Learning", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2825-283, 27.
BYU ScholarsArchive Citation
Lundell, Jared and Ventura, Dan A., "A Data-dependent Distance Measure for Transductive Instance-based Learning" (2007). Faculty Publications. 942.
https://scholarsarchive.byu.edu/facpub/942
Document Type
Peer-Reviewed Article
Publication Date
2007-10-07
Permanent URL
http://hdl.lib.byu.edu/1877/2515
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/