A graph based transductive ranking algorithm.

FSKD（2012）

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摘要

Semi-supervised ranking is a newly developed machine learning problem. In this paper, based on the graph constructed on both labeled and unlabeled data points, we propose a novel semi-supervised ranking algorithm in the transdutive setting via a semi-supervised regression model. We also derive the solution in an explicit form for this model. Experiments on two QSAR data sets demonstrate its utility and effectiveness. © 2012 IEEE.

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关键词

graph laplacian,quantitative structure-activity relationship,ranking,semi-supervised learning,quantitative structure activity relationship,qsar,graph theory,machine learning,regression analysis,learning artificial intelligence,semi supervised learning,regression model

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