%0 Journal Article %1 vonluxburg2008statistical %A von Luxburg, Ulrike %A Schoelkopf, Bernhard %D 2008 %K learning mathematics readings stats theory %T Statistical Learning Theory: Models, Concepts, and Results %U http://arxiv.org/abs/0810.4752 %X Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.

@article{vonluxburg2008statistical, abstract = {Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.}, added-at = {2020-10-17T17:13:19.000+0200}, author = {von Luxburg, Ulrike and Schoelkopf, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/281cf0e86b93210e03cd631f493f7a361/kirk86}, description = {[0810.4752] Statistical Learning Theory: Models, Concepts, and Results}, interhash = {c01e40a29d82efae067c3bf062e721f1}, intrahash = {81cf0e86b93210e03cd631f493f7a361}, keywords = {learning mathematics readings stats theory}, note = {cite arxiv:0810.4752}, timestamp = {2020-10-17T17:13:19.000+0200}, title = {Statistical Learning Theory: Models, Concepts, and Results}, url = {http://arxiv.org/abs/0810.4752}, year = 2008 }