Statistical Learning Theory: Models, Concepts, and Results

Part of book or chapter of book, Preprint OPEN
von Luxburg, Ulrike; Schoelkopf, Bernhard;
(2011)

Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide populari... View more
  • References (5)

    D. Cor eld, B. Scholkopf, and V. Vapnik. Popper, Falsi cation, and the VC-Dimension. Technical Report TR-145, Max Planck Institute For Biological Cybernetics, 2005.

    M. Tipping. Bayesian inference: An introduction to principles and practice in machine learning. In O. Bousquet, U. von Luxburg, and G. Ratsch, editors, Advanced Lectures on Machine Learning, pages 41{62. Springer, 2003.

    V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16:264 { 280, 1971.

    D. Wolpert. The supervised learning no-free-lunch theorems. In Proc. 6th Online World Conf. on Soft Computing in Industrial Applications, 2001.

    D. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Trans. Evolutionary Computation, 1(1):67{82, 1997.

  • Metrics
Share - Bookmark