Transductive Classification via Local Learning Regularization

Mingrui Wu, Bernhard Schölkopf
; Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:628-635, 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v2-wu07a, title = {Transductive Classification via Local Learning Regularization}, author = {Mingrui Wu and Bernhard Schölkopf}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {628--635}, year = {2007}, editor = {Marina Meila and Xiaotong Shen}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/wu07a/wu07a.pdf}, url = {http://proceedings.mlr.press/v2/wu07a.html}, 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.} }
Endnote
%0 Conference Paper %T Transductive Classification via Local Learning Regularization %A Mingrui Wu %A Bernhard Schölkopf %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-wu07a %I PMLR %J Proceedings of Machine Learning Research %P 628--635 %U http://proceedings.mlr.press %V 2 %W PMLR %X 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.
RIS
TY - CPAPER TI - Transductive Classification via Local Learning Regularization AU - Mingrui Wu AU - Bernhard Schölkopf BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics PY - 2007/03/11 DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-wu07a PB - PMLR SP - 628 DP - PMLR EP - 635 L1 - http://proceedings.mlr.press/v2/wu07a/wu07a.pdf UR - http://proceedings.mlr.press/v2/wu07a.html AB - 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. ER -
APA
Wu, M. & Schölkopf, B.. (2007). Transductive Classification via Local Learning Regularization. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in PMLR 2:628-635

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