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Semi-Supervised Classification Using Gaussian Processes

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving semi-supervised binary classification problem using GP regression (GPR) models. The algorithms are closely related to semi-supervised classification based on support vector regression (SVR) and maximum margin clustering. The proposed algorithms are simple and easy to implement. Also, the hyper-parameters are estimated without resorting to expensive cross-validation technique. The algorithm based on sparse GPR model gives a sparse solution directly unlike the SVR based algorithm. Use of sparse GPR model helps in making the proposed algorithm scalable. The results of experiments on synthetic and real-world datasets demonstrate the efficacy of proposed sparse GP based algorithm for semi-supervised classification.

  1. http://hdl.handle.net/2005/658
Identiferoai:union.ndltd.org:IISc/oai:etd.ncsi.iisc.ernet.in:2005/658
Date01 1900
CreatorsPatel, Amrish
ContributorsShevade, S K
Source SetsIndia Institute of Science
Languageen_US
Detected LanguageEnglish
TypeThesis
RelationG22961

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