Editing Statistical learning theory (section)

{{short description|Framework for machine learning}}
{{about|statistical learning in machine learning|its use in psychology|Statistical learning in language acquisition}}
{{see also|Computational learning theory}}
{{Machine learning|Theory}}
'''Statistical learning theory''' is a framework for [[machine learning]] drawing from the fields of [[statistics]] and [[functional analysis]].<ref>{{cite book | author-link = Vladimir Vapnik | first = Vladimir N. | last = Vapnik | year = 1995 | title = The Nature of Statistical Learning Theory | publisher = Springer | location = New York | isbn = 978-1-475-72440-0}}</ref><ref>{{cite book | last1 = Hastie | first1 = Trevor | author1-link = Trevor Hastie | title = The Elements of Statistical Learning: Data Mining, Inference, and Prediction | last2 = Tibshirani | first2 = Robert | last3 = Friedman |first3 = Jerome H. | date = 2009 | publisher = Springer | isbn = 978-0-387-84857-0 | series = Springer Series in Statistics | location = New York, NY}}</ref><ref>{{Cite Mehryar Afshin Ameet 2012}}</ref> Statistical learning theory deals with the [[statistical inference]] problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as [[computer vision]], [[speech recognition]], and [[bioinformatics]].