{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-prediction-with-svm-models-containing","title":"Fast Prediction with SVM Models Containing RBF Kernels","arxiv_id":"1403.0736","date":"2014-03-04","proceeding":null,"authors":["Marc Claesen","Frank De Smet","Johan A. K. Suykens","Bart De Moor"],"abstract":"We present an approximation scheme for support vector machine models that use\nan RBF kernel. A second-order Maclaurin series approximation is used for\nexponentials of inner products between support vectors and test instances. The\napproximation is applicable to all kernel methods featuring sums of kernel\nevaluations and makes no assumptions regarding data normalization. The\nprediction speed of approximated models no longer relates to the amount of\nsupport vectors but is quadratic in terms of the number of input dimensions. If\nthe number of input dimensions is small compared to the amount of support\nvectors, the approximated model is significantly faster in prediction and has a\nsmaller memory footprint. An optimized C++ implementation was made to assess\nthe gain in prediction speed in a set of practical tests. We additionally\nprovide a method to verify the approximation accuracy, prior to training models\nor during run-time, to ensure the loss in accuracy remains acceptable and\nwithin known bounds.","url_abs":"http://arxiv.org/abs/1403.0736v3","url_pdf":"http://arxiv.org/pdf/1403.0736v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-prediction-with-svm-models-containing","repo_url":"https://github.com/claesenm/approxsvm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}