Agresti, A. Categorical Data Analysis, 2nd Ed. John Wiley & Sons, Inc.: Hoboken, NJ, 2002.
 Allwein, E., R. Schapire, and Y. Singer. "Reducing multiclass to binary: A unifying approach for margin classiﬁers." Journal of Machine Learning Research. Vol. 1, 2000, pp. 113–141.
 Alpaydin, E. "Combined 5 x 2 CV F Test for Comparing Supervised Classification Learning Algorithms." Neural Computation, Vol. 11, No. 8, pp. 1885–1992, 1999.
 Blackard, J. A. and D. J. Dean. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture 24, pp. 131–151, 1999.
 Bottou, L., and Chih-Jen Lin. Support
Vector Machine Solvers. Available at
 Bouckaert. R. "Choosing Between Two Learning Algorithms Based on Calibrated Tests." International Conference on Machine Learning, pp. 51–58, 2003.
 Bouckaert, R. and E. Frank. "Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms." In Advances in Knowledge Discovery and Data Mining, 8th Pacific-Asia Conference, pp. 3–12, 2004.
 Breiman, L. Bagging Predictors. Machine Learning 26, pp. 123–140, 1996.
 Breiman, L. Random Forests. Machine Learning 45, pp. 5–32, 2001.
 Breiman, L.
 Breiman, L., et al. Classification and Regression Trees. Chapman & Hall, Boca Raton, 1993.
 Christianini, N., and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK, 2000.
 Dietterich, T. "Approximate statistical tests for comparing supervised classification learning algorithms." Neural Computation, Vol. 10, No. 7: pp. 1895–1923, 1998.
 Dietterich, T., and G. Bakiri. "Solving Multiclass Learning Problems Via Error-Correcting Output Codes." Journal of Artificial Intelligence Research. Vol. 2, 1995, pp. 263–286.
 Escalera, S., O. Pujol, and P. Radeva. "On the decoding process in ternary error-correcting output codes." IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 32, Issue 7, 2010, pp. 120–134.
 Escalera, S., O. Pujol, and P. Radeva. "Separability of ternary codes for sparse designs of error-correcting output codes." Pattern Recogn. Vol. 30, Issue 3, 2009, pp. 285–297.
 Fan, R.-E., P.-H. Chen, and C.-J. Lin. "Working set selection using second order information for training support vector machines." Journal of Machine Learning Research, Vol 6, 2005, pp. 1889–1918.
 Fagerlan, M.W., S Lydersen, P. Laake. "The McNemar Test for Binary Matched-Pairs Data: Mid-p and Asymptotic Are Better Than Exact Conditional." BMC Medical Research Methodology. Vol. 13, 2013, pp. 1–8.
 Freund, Y. A more robust boosting algorithm. arXiv:0905.2138v1, 2009.
 Freund, Y. and R. E. Schapire. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. of Computer and System Sciences, Vol. 55, pp. 119–139, 1997.
 Friedman, J. Greedy function approximation: A gradient boosting machine. Annals of Statistics, Vol. 29, No. 5, pp. 1189–1232, 2001.
 Friedman, J., T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Annals of Statistics, Vol. 28, No. 2, pp. 337–407, 2000.
 Hastie, T., and R. Tibshirani. "Classification by Pairwise Coupling." Annals of Statistics. Vol. 26, Issue 2, 1998, pp. 451–471.
 Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, second edition. Springer, New York, 2008.
 Ho, C. H. and C. J. Lin. "Large-Scale Linear Support Vector Regression." Journal of Machine Learning Research, Vol. 13, 2012, pp. 3323–3348.
 Ho, T. K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 832–844, 1998.
 Hsieh, C. J., K. W. Chang, C. J. Lin, S. S. Keerthi, and S. Sundararajan. "A Dual Coordinate Descent Method for Large-Scale Linear SVM." Proceedings of the 25th International Conference on Machine Learning, ICML '08, 2001, pp. 408–415.
 Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen
Lin. A Practical Guide to Support Vector Classification.
 Hu, Q., X. Che, L. Zhang, and D. Yu. "Feature Evaluation and Selection Based on Neighborhood Soft Margin." Neurocomputing. Vol. 73, 2010, pp. 2114–2124.
 Kecman V., T. -M. Huang, and M. Vogt. "Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance." In Support Vector Machines: Theory and Applications. Edited by Lipo Wang, 255–274. Berlin: Springer-Verlag, 2005.
 Kohavi, R. "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid." Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
 Lancaster, H.O. "Significance Tests in Discrete Distributions." JASA, Vol. 56, Number 294, 1961, pp. 223–234.
 Langford, J., L. Li, and T. Zhang. "Sparse Online Learning Via Truncated Gradient." J. Mach. Learn. Res., Vol. 10, 2009, pp. 777–801.
 McNemar, Q. "Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages." Psychometrika, Vol. 12, Number 2, 1947, pp. 153–157.
 Mosteller, F. "Some Statistical Problems in Measuring the Subjective Response to Drugs." Biometrics, Vol. 8, Number 3, 1952, pp. 220–226.
 Nocedal, J. and S. J. Wright. Numerical Optimization, 2nd ed., New York: Springer, 2006.
 Schapire, R. E. et al. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, Vol. 26, No. 5, pp. 1651–1686, 1998.
 Schapire, R., and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, Vol. 37, No. 3, pp. 297–336, 1999.
 Shalev-Shwartz, S., Y. Singer, and N. Srebro. "Pegasos: Primal Estimated Sub-Gradient Solver for SVM." Proceedings of the 24th International Conference on Machine Learning, ICML '07, 2007, pp. 807–814.
 Seiffert, C., T. Khoshgoftaar, J. Hulse, and A. Napolitano. RUSBoost: Improving clasification performance when training data is skewed. 19th International Conference on Pattern Recognition, pp. 1–4, 2008.
 Warmuth, M., J. Liao, and G. Ratsch. Totally corrective boosting algorithms that maximize the margin. Proc. 23rd Int'l. Conf. on Machine Learning, ACM, New York, pp. 1001–1008, 2006.
 Wu, T. F., C. J. Lin, and R. Weng. "Probability Estimates for Multi-Class Classification by Pairwise Coupling." Journal of Machine Learning Research. Vol. 5, 2004, pp. 975–1005.
 Wright, S. J., R. D. Nowak, and M. A. T. Figueiredo. "Sparse Reconstruction by Separable Approximation." Trans. Sig. Proc., Vol. 57, No 7, 2009, pp. 2479–2493.
 Xiao, Lin. "Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization." J. Mach. Learn. Res., Vol. 11, 2010, pp. 2543–2596.
 Xu, Wei. "Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent." CoRR, abs/1107.2490, 2011.
 Zadrozny, B. "Reducing Multiclass to Binary by Coupling Probability Estimates." NIPS 2001: Proceedings of Advances in Neural Information Processing Systems 14, 2001, pp. 1041–1048.
 Zadrozny, B., J. Langford, and N. Abe. Cost-Sensitive
Learning by Cost-Proportionate Example Weighting. CiteSeerX.
 Zhou, Z.-H. and X.-Y. Liu. On
Multi-Class Cost-Sensitive Learning. CiteSeerX. [Online]