A Continuation Method for Semi-supervised SVMs. Chapelle, O., Chi, M., & Zien, A. In Proceedings of the International Conference on Machine Learning, pages 185--192, 2006. abstract bibtex Semi-Supervised Support Vector Machines (S 3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.
@InProceedings{Chapelle2006,
Title = {A Continuation Method for Semi-supervised SVMs},
Author = {Chapelle, O. and Chi, M. and Zien, A.},
Booktitle = {Proceedings of the International Conference on Machine Learning},
Year = {2006},
Pages = {185--192},
Abstract = {Semi-Supervised Support Vector Machines
(S
3
VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries
which do not cut clusters. However their
main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a
global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the
same objective function, our continuation
method often leads to lower test errors.},
Timestamp = {2014.10.24}
}
Downloads: 0
{"_id":"9otXchmwnT8RB7Zy4","bibbaseid":"chapelle-chi-zien-acontinuationmethodforsemisupervisedsvms-2006","downloads":0,"creationDate":"2017-09-14T16:34:36.304Z","title":"A Continuation Method for Semi-supervised SVMs","author_short":["Chapelle, O.","Chi, M.","Zien, A."],"year":2006,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/jfslin/jfslin.github.io/master/jf2lin.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"A Continuation Method for Semi-supervised SVMs","author":[{"propositions":[],"lastnames":["Chapelle"],"firstnames":["O."],"suffixes":[]},{"propositions":[],"lastnames":["Chi"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Zien"],"firstnames":["A."],"suffixes":[]}],"booktitle":"Proceedings of the International Conference on Machine Learning","year":"2006","pages":"185--192","abstract":"Semi-Supervised Support Vector Machines (S 3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.","timestamp":"2014.10.24","bibtex":"@InProceedings{Chapelle2006,\n Title = {A Continuation Method for Semi-supervised SVMs},\n Author = {Chapelle, O. and Chi, M. and Zien, A.},\n Booktitle = {Proceedings of the International Conference on Machine Learning},\n Year = {2006},\n Pages = {185--192},\n\n Abstract = {Semi-Supervised Support Vector Machines\n(S\n3\nVMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries\nwhich do not cut clusters. However their\nmain problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a\nglobal optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the\nsame objective function, our continuation\nmethod often leads to lower test errors.},\n Timestamp = {2014.10.24}\n}\n\n","author_short":["Chapelle, O.","Chi, M.","Zien, A."],"key":"Chapelle2006","id":"Chapelle2006","bibbaseid":"chapelle-chi-zien-acontinuationmethodforsemisupervisedsvms-2006","role":"author","urls":{},"downloads":0},"search_terms":["continuation","method","semi","supervised","svms","chapelle","chi","zien"],"keywords":[],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}