Alpha seeding for support vector machines

Published 1 August 2000

Dennis DeCoste, Kiri L. Wagstaff

Citations81

TL;DR

A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM's generally scale quadratically (or worse) in the number of examples or support vectors.

Abstract

Article Alpha seeding for support vector machines Share on Authors: Dennis DeCoste Machine Learning Systems Group, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA Machine Learning Systems Group, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CAView Profile , Kiri Wagstaff Department of Computer Science, Cornell University, 4156 Upson Hall, Ithaca, NY Department of Computer Science, Cornell University, 4156 Upson Hall, Ithaca, NYView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 345–349https://doi.org/10.1145/347090.347165Online:01 August 2000Publication History 42citation550DownloadsMetricsTotal Citations42Total Downloads550Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Keywords

Computer Science

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