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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“Nature will not let us fret and fume. She does not like our benevolence or our learning much better than she likes our frauds and wars. When we come out of the caucus, or the bank, or the abolition-convention, or the temperance-meeting, or the transcendental club, into the fields and woods, she says to us, “so hot? my little Sir.””
—Ralph Waldo Emerson (1803–1882)
“No theory is good unless it permits, not rest, but the greatest work. No theory is good except on condition that one use it to go on beyond.”
—André Gide (1869–1951)