1. Adler, I., Shamir, R.: A randomized scheme for speeding up algorithms for linear and convex programming with high constraints-to-variable ratio. Math. Program. 61, 39–52 (1993)
2. Lecture Notes in Computer Science;J.L. Balcázar,2001
3. Balcázar, J.L., Dai, Y., Watanabe, O.: Provably fast training algorithms for support vector machines. In: Proceedings of First IEEE International Conference on Data Mining (ICDM’01), pp. 43–50. IEEE, Los Alamitos (2001)
4. Balcázar, J.L., Dai, Y., Watanabe, O.: Provably fast support vector regression using random sampling. In: Proceedings of SIAM Workshop in Discrete Mathematics and Data Mining, pp. 19–29. SIAM, Philadelphia (2002)
5. Bennett, K.P., Bredensteiner, E.J.: Duality and geometry in SVM classifiers. In: Proceedings of 17th International Conference on Machine Learning (ICML’2000), pp. 57–64. Morgan Kaufmann, San Mateo (2000)