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
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“...I didn’t consider intellectuals intelligent, I never liked them or their thoughts about life. I defined them as people who care nothing for argument, who are interested only in information; or as people who have a preference for learning things rather than experiencing them. They have opinions but no point of view.... Their talk is the gloomiest type of human discourse I know.... This is a red flag to my nature. Intellectuals, to me have no natures ...”
—Margaret Anderson (1886–1973)
“The whole theory of modern education is radically unsound. Fortunately in England, at any rate, education produces no effect whatsoever. If it did, it would prove a serious danger to the upper classes, and probably lead to acts of violence in Grosvenor Square.”
—Oscar Wilde (1854–1900)