论文信息 - A Practical Guide to Support Vector Classification

A Practical Guide to Support Vector Classification

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 propose a simple procedure, which usually gives reasonable results.

Chih-Chung Chang | Chih-Jen Lin | Chih-Wei Hsu

[1] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.

[2] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .

[3] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[4] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[5] Ke Wang,et al. PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteria , 2003, Nucleic Acids Res..

[6] B. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.

[7] Hsuan-Tien Lin. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[8] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.