%0 Journal Article %1 keyhere %A Smola, Alex J. %A Schölkopf, Bernhard %D 2004 %J Statistics and Computing %K learning machine %N 3 %P 199--222 %T A tutorial on support vector regression %U http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88 %V 14 %X In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective. ER -

@article{keyhere, abstract = {In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective. ER -}, added-at = {2008-04-02T15:20:06.000+0200}, author = {Smola, Alex J. and Schölkopf, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/2073120e9de7a25b475c38f097600f045/utahell}, description = {SpringerLink - Zeitschriftenbeitrag}, interhash = {6c114cfbcf7e1e1b7be3410382966f87}, intrahash = {073120e9de7a25b475c38f097600f045}, journal = {Statistics and Computing}, keywords = {learning machine}, month = {#aug#}, number = 3, pages = {199--222}, timestamp = {2009-08-13T15:35:36.000+0200}, title = {A tutorial on support vector regression}, url = {http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88}, volume = 14, year = 2004 }