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| A Practical Guide to Support Vector Classification (from LibSVM authors) Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin - Support vector machine (SVM) is a popular technique for classification.However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we proposea simple procedure which usually gives reasonable results.
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| A Tutorial on Support Vector Machines for Pattern CHRISTOPHER J.C. BURGES - The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail.
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| Core vector machines: Fast SVM training on very large data sets. I.W. Tsang, J.T. Kwok, P.-M. Cheung. |
| Efficient Algorithms for Ranking with SVMs O. Chapelle, S. S. Keerthi |
| Pegasos: Primal Estimated sub-GrAdient SOlver for SVM Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro |
| Support Vector Machine Parameter Optimization for Text Categorization Problems Ageev M.S., Dobrov B.V. |
| Support Vector Machines Ovidiu Ivanciuc |
| Support Vector Machines (SVMs) |
| Support Vector Machines in R Alexandros Karatzoglou, David Meyer, Kurt Hornik |
| SVM Tutorial: Classification, Regression, and Ranking Hwanjo Yu, Sungchul Kim |
| SVM tutorials |
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