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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“If you should put a knife into a French girl’s learning it would explode and blow away like an omelette soufflee ...”
—M. E. W. Sherwood (1826–1903)
“There is in him, hidden deep-down, a great instinctive artist, and hence the makings of an aristocrat. In his muddled way, held back by the manacles of his race and time, and his steps made uncertain by a guiding theory which too often eludes his own comprehension, he yet manages to produce works of unquestionable beauty and authority, and to interpret life in a manner that is poignant and illuminating.”
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