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    Semi-Supervised Classification Using Gaussian Processes

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    G22961.pdf (508.7Kb)
    Date
    2010-03-26
    Author
    Patel, Amrish
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    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
    https://etd.iisc.ac.in/handle/2005/658
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    • Computer Science and Automation (CSA) [392]

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