Description: In this tutorial we present a brief introduction to SVM, and we discuss about SVM published papers, workshop materials & material collected books and material available online on the World Wide Web. In the beginning we try to define SVM and try to talk as why SVM, with a brief overview of statistical learning theory. The mathematical formulation of SVM is presented, and theory for the implementation of SVM is briefly discussed. Finally some conclusions on SVM and application areas are included. Support Vector Machines (SVMs) are competing with Neural Networks as tools for solving pattern recognition problems. This tutorial assumes you are familiar with concepts of Linear Algebra, real analysis and also understand the working of neural networks and have some background in AI.-In this tutorial we present a brief introduction to SVM, and we discuss about SVM published papers, workshop materials & material collected books and material available online on the World Wide Web. In the beginning we try to define SVM and try to talk as why SVM, with a brief overview of statistical learning theory. The mathematical formulation of SVM is presented, and theory for the implementation of SVM is briefly discussed. Finally some conclusions on SVM and application areas are included. Support Vector Machines (SVMs) are competing with Neural Networks as tools for solving pattern recognition problems. This tutorial assumes you are familiar with concepts of Linear Algebra, real analysis and also understand the working of neural networks and have some background in AI. Platform: |
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