Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the 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. It is the theoretical framework underlying support vector machines.
Read more about Statistical Learning Theory: Introduction, Formal Description, Loss Functions, Regularization
Famous quotes containing the words learning and/or theory:
“Experiences in order to be educative must lead out into an expanding world of subject matter, a subject matter of facts or information and of ideas. This condition is satisfied only as the educator views teaching and learning as a continuous process of reconstruction of experience.”
—John Dewey (1859–1952)
“... the first reason for psychology’s failure to understand what people are and how they act, is that clinicians and psychiatrists, who are generally the theoreticians on these matters, have essentially made up myths without any evidence to support them; the second reason for psychology’s failure is that personality theory has looked for inner traits when it should have been looking for social context.”
—Naomi Weisstein (b. 1939)