machine learning, instance-based
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). All Faculty Publications. 942.
Physical and Mathematical Sciences
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