This article is about statistical learning in machine learning. For its use in psychology, see Statistical learning in language acquisition.
See also: Computational learning theory
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Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis.[1][2][3] 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.
^Vapnik, Vladimir N. (1995). The Nature of Statistical Learning Theory. New York: Springer. ISBN 978-1-475-72440-0.
^Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. New York, NY: Springer. ISBN 978-0-387-84857-0.
^Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. US, Massachusetts: MIT Press. ISBN 9780262018258.
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Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statisticallearningtheory has led to...
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empiricist theories of language acquisition include the statisticallearningtheory. Charles F. Hockett of language acquisition, relational frame theory, functionalist...
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