Support Vector Machines
Vapnik and Cortes published their work on Support Vector Machines (SVMs), a method for finding maximum-margin decision boundaries in high-dimensional spaces with unusually strong theoretical guarantees. SVMs quickly became one of the leading approaches for classification problems across text, vision, and bioinformatics.
Vladimir VapnikCorinna CortesAT&T Bell Labs