{"type":"ScholarlyArticle","title":"Semi-Supervised Classification Using Sparse Gaussian Process Regression","authors":[{"name":"Amrish Patel","slug":"amrish-patel","given":"Amrish","family":"Patel","affiliation":"Computer Science and Automation Indian Institute of Science , India"},{"name":"S. Sundararajan","slug":"s-sundararajan","given":"S.","family":"Sundararajan","affiliation":"Yahoo! Labs Bangalore , India"},{"name":"Shirish K. Shevade","slug":"shirish-k-shevade","given":"Shirish K.","family":"Shevade","affiliation":"Computer Science and Automation Indian Institute of Science , India"}],"abstract":"Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.Amrish Patel, S. Sundararajan, Shirish Shevade","doi":null,"url":"https://ijcai.org/proceedings/2009/201","pdf":"https://ijcai.org/proceedings/2009/0201.pdf","markdown":"https://ijcai.org/proceedings/2009/201.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/201","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":201,"pages":{"first":1193,"last":1197},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Asuncion and Newman, 2007] A. Asuncion and D.J. New- man. 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