- Anscombe’s quartet on Wikipedia
- Support Vector Machines on scikit learn
- Support Vector Regression (SVR) using linear and non-linear kernels on scikit learn
- SVM without tears by Ankit Sharma on Digg Data
- Least squares support vector machines on Wikipedia
- An Idiot’s Guide to Support Vector Machines by R. Berwick
- Support Vector Machine (and Statistical Learning Theory) Tutorial by Jason Weston
- Support vector regression for real-time flood stage forecasting by Pao-Shan Yu, Shien-Tsung Chen and I-Fan Chang
- Least-squares SVM regression on Optunity
- Data Science for Business by Foster and Provost
- Support Vector Machines on CRAN
- Support Vector Regression on SVM Tutorial
- Practical session: Introduction to SVM in R by Jean-Philippe Vert
- R Example Code – iris dataset
- Python Code Example – iris dataset
- e1071 package documentation by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel, Friedrich Leisch, Chih-Chung Chang, Chih-Chen Lin
- kernlab documentation by A. Karatzoglou
- Duality, Geometry, and Support Vector Regression by Jinbo Bi and Kristin P. Bennett
- Support Vector Machines and Learning on Documents in
*Introduction to Information Retrieval*, Cambridge University Press 2008, by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze - Support Vector Machines on Wikipedia
- A tutorial on support vector regression, by ALEX J. SMOLA and BERNHARD SCHOLKOPF

##### Support Vector Machines – Posts

- Overview
- Classification
- Kernel Transformations
- Non-Separable Data
- Further Considerations
- Examples [previous]
- References [current]