A tutorial on support vector regression

@misc{citeulike:1221089, abstract = {In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for regression and 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 and capacity...}, author = {Smola, A. and Sch, B. }, citeulike-article-id = {1221089}, keywords = {kernel\_methods, machine\_learning, support\_vector\_machines}, posted-at = {2007-11-10 12:35:11}, priority = {0}, title = {A tutorial on support vector regression}, url = {http://citeseer.ist.psu.edu/203652.html}, year = {1998} }

TY - GEN ID - citeulike:1221089 L3 - citeulike-article-id:1221089 TI - A tutorial on support vector regression N2 - In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for regression and 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 and capacity... KW - kernel_methods KW - machine_learning KW - support_vector_machines AU - Smola, A AU - Sch, B PY - 1998/// UR - http://citeseer.ist.psu.edu/203652.html ER -