PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transductive Classification via Local Learning Regularization
M. Wu and B. Schölkopf
In: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS)(2007).

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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4030
Deposited By:Bernhard Schölkopf
Deposited On:25 February 2008