By Thorsten Joachims
Based on rules from aid Vector Machines (SVMs), Learning to categorise textual content utilizing aid Vector Machines provides a brand new method of producing textual content classifiers from examples. The method combines excessive functionality and potency with theoretical figuring out and enhanced robustness. specifically, it truly is powerful with out grasping heuristic elements. The SVM process is computationally effective in education and class, and it comes with a studying concept which can advisor real-world applications.
Learning to categorise textual content utilizing help Vector Machines provides an entire and distinct description of the SVM method of studying textual content classifiers, together with education algorithms, transductive textual content category, effective functionality estimation, and a statistical studying version of textual content type. moreover, it contains an outline of the sphere of textual content type, making it self-contained even for rookies to the sphere. This publication provides a concise creation to SVMs for development acceptance, and it encompasses a special description of the way to formulate text-classification initiatives for computing device learning.
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Additional info for Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science) by Thorsten Joachims