Support Vector Machines (SVM)

Why It Was Important

Developed by Vladimir Vapnik and Corinna Cortes at AT&T Bell Labs, SVMs identify the optimal hyperplane that maximizes the margin between different classes of data. Using the 'kernel trick', they efficiently handle non-linear data, dominating machine learning competitions until the late 2000s deep learning resurgence.

Who Invented It

Vladimir Vapnik & Corinna Cortes

Data scientists specializing in statistical learning theory.

Applications

• Text Categorization
• Image Classification
• Bioinformatics