Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.
Ben-HurA., and WestonJ.2010. A user's guide to support vector machines. In Data Mining Techniques for the Life Sciences, ed. CarugoO., and EisenhaberF., 223–239. New York: Humana Press.
2.
ChangC.-C., and LinC.-J.2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology2(3): Article 27.
3.
ChenP.-H., LinC.-J., and SchölkopfB.2005. A tutorial on ν-support vector machines. Applied Stochastic Models in Business and Industry21: 111–136.
4.
CortesC., and VapnikV.1995. Support-vector networks. Machine Learning20: 273–297.
HsuC.-W., and LinC.-J.2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks13: 415–425.
9.
JamesG., WittenD., HastieT., and TibshiraniR.2013. An Introduction to Statistical Learning: With Applications in R.New York: Springer.
10.
JoachimsT.1999. Making large-scale support vector machine learning practical. In Advances in Kernel Methods: Support Vector Learning, ed. SchölkopfB., BurgesC. J. C., and SmolaA. J., 169–184. Cambridge, MA: MIT Press.
LinH.-T., LinC.-J., and WengR. C.2007. A note on Platt's probabilistic outputs for support vector machines. Machine Learning68: 267–276.
14.
MarocoJ., SilvaD., RodriguesA., GuerreiroM., SantanaI., and de MendonçaA.2011. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes4: Article 299.
15.
PlattJ. C.2000. Probabilities for SV machines. In Advances in Large Margin Classifiers, ed. SmolaA. J., BartlettP. L., SchölkopfB., and SchuurmansD., 61–74. Cambridge, MA: MIT Press.
16.
SchölkopfB., PlattJ. C., Shawe-TaylorJ., SmolaA. J., and WilliamsonR. C.2001. Estimating the support of a high-dimensional distribution. Neural Computation13: 1443–1471.
17.
SchölkopfB., SmolaA. J., WilliamsonR. C., and BartlettP. L.2000. New support vector algorithms. Neural Computation12: 1207–1245.
18.
SchonlauM.2005. Boosted regression (boosting): An introductory tutorial and a Stata plugin. Stata Journal5: 330–354.
19.
SmolaA. J., and SchölkopfB.2004. A tutorial on support vector regression. Statistics and Computing14: 199–222.
20.
VapnikV. N.2000. The Nature of Statistical Learning Theory. 2nd ed. New York: Springer.
21.
VerplanckeT., Van LooyS., BenoitD., VansteelandtP., DepuydtP., De TurckF., and DecruyenaereJ.2008. Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Medical Informatics and Decision Making8: Article 56.
22.
WuT.-F., LinC.-J., and WengR. C.2004. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research5: 975–1005.
