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This page contains the titles and abstracts of papers written by author Lundell, Jared, a member of the BYU Neural Networks and Machine Learning (NNML) Research Group. Postscript files are available for most papers. A more concise list is available.
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A Data-dependent Distance Measure for Transductive Instance-based Learning
- Authors: Jared Lundell and Dan Ventura
- 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.
- Reference: In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pages 2825–2830, October 2007.
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