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Please use this identifier to cite or link to this item: http://hdl.handle.net/2005/658

Title: Semi-Supervised Classification Using Gaussian Processes
Authors: Patel, Amrish
Advisors: Shevade, S K
Keywords: Classification (A I)
Gaussian Processes
Gaussian Process Regression (GPR)
Semi-supervised Classification - Algorithms
Support Vector Regression (SVR)
Classification Models
Semi-supervised Learning
Submitted Date: Jan-2009
Series/Report no.: G22961
Abstract: 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.
URI: http://hdl.handle.net/2005/658
Appears in Collections:Computer Science and Automation (csa)

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