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:
“I thought a minute, and says to myself, hold on,—s’pose you’d a done right and give Jim up; would you felt better than what you do now? No, says I, I’d feel bad—I’d feel just the same way I do now. Well, then, says I, what’s the use you learning to do right, when it’s troublesome to do right and ain’t no trouble to do wrong, and the wages is just the same?”
—Mark Twain [Samuel Langhorne Clemens] (1835–1910)
“Frankly, these days, without a theory to go with it, I can’t see a painting.”
—Tom Wolfe (b. 1931)