How did Generalized Portrait method shape concepts of support vector method?
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The Generalized Portrait method, developed by Vapnik and Chervonenkis, played a significant role in shaping the concepts of the Support Vector Machine (SVM) . The ideas proposed in the Generalized Portrait method, starting in 1962 and published in 1964, laid the foundation for SVMs and their application in computer learning and pattern recognition . The SVM is a supervised learning technique for classification that has gained popularity in various fields, including data mining, engineering, and bioinformatics . The method utilizes the concept of an optimal separating hyperplane and can be extended to handle nonlinear classification problems through the use of kernels . The SVM's effectiveness and versatility have made it a state-of-the-art machine learning method, with applications ranging from surface approximation to 3D deformation fields and object morphing .
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