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Conference Paper

Transductive Classification via Local Learning Regularization

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84321

Wu,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Wu, M., & Schölkopf, B. (2007). Transductive Classification via Local Learning Regularization. Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), 628-635.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE7F-8
Abstract
The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive Classification (TC) problems. Specifically, we formulate a Local Learning Regularizer (LL-Reg) which leads to a solution with the property that the label of each data point can be well predicted based on its neighbors and their labels. For model selection, an efficient way to compute the leave-one-out classification error is provided for the proposed and related algorithms. Experimental results using several benchmark datasets illustrate the effectiveness of the proposed approach.