Books on the topic 'Statistical learning theory'
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Vapnik, Vladimir Naumovich. Statistical learning theory. New York: Wiley, 1998.
Find full textEmmert-Streib, Frank, and Matthias Dehmer, eds. Information Theory and Statistical Learning. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-84816-7.
Full textEmmert-Streib, Frank. Information Theory and Statistical Learning. Boston, MA: Springer US, 2009.
Find full textVapnik, Vladimir N. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3264-1.
Full textCatoni, Olivier. Statistical Learning Theory and Stochastic Optimization. Edited by Jean Picard. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b99352.
Full textVapnik, Vladimir N. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2440-0.
Full textVapnik, Vladimir Naumovich. The nature of statistical learning theory. New York: Springer, 1995.
Find full textWatanabe, Sumio. Algebraic geometry and statistical learning theory. Cambridge: Cambridge University Press, 2009.
Find full textVapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.
Find full textKulkarni, Sanjeev, and Gilbert Harman. An Elementary Introduction to Statistical Learning Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118023471.
Full textKulkarni, Sanjeev. An elementary introduction to statistical learning theory. Hoboken, N.J: Wiley, 2011.
Find full textHarman, Gilbert. Reliable reasoning: Induction and statistical learning theory. Cambridge, MA: MIT Press, 2007.
Find full textTurdaliev, Nurlan. Calibration and Bayesian learning. [Minneapolis, Minn.]: Federal Reserve Bank of Minneapolis, 1999.
Find full textResearch Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Find full textR, Sunstein Cass, and John F. Kennedy School of Government, eds. Extremism and social learning. Cambridge, Mass: John F. Kennedy School of Government, Harvard University, 2008.
Find full textBikhchandani, Sushil. Stochastic dominance under bayesian learning. Toronto: University of Toronto, 1990.
Find full textKersting, Kristian. An inductive logic programming approach to statistical relational learning. Amsterdam: IOS Press, 2006.
Find full textSeppo, Honkapohja, ed. Learning & Expectations in Macroeconomics. S. l: Princeton University Press, 2001.
Find full textNeapolitan, Richard E. Learning Bayesian networks. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
Find full textErnest, Fokoué, Zhang Hao Helen, and SpringerLink (Online service), eds. Principles and Theory for Data Mining and Machine Learning. New York, NY: Springer-Verlag New York, 2009.
Find full textNeal, Radford M. Bayesian learning for neural networks. Toronto: University of Toronto, Dept. of Computer Science, 1995.
Find full textNeal, Radford M. Bayesian learning for neural networks. New York: Springer, 1996.
Find full textJovanovic, Boyan. The Bayesian foundations of learning by doing. Cambridge, MA: National Bureau of Economic Research, 1994.
Find full text1945-, Basak Subhash C., ed. Statistical and machine learning approaches for network analysis. Hoboken, N.J: Wiley, 2012.
Find full textBarber, David. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press, 2011.
Find full textACM Workshop on Computational Learning Theory (5th 1992 Pittsburgh, Pennsylvania). Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, July 27-29, 1992, Pittsburgh, Pennsylvania. New York, N.Y: Association for Computing Machinery, 1992.
Find full textAttilio, Giordana, and Cornuejols Antoine, eds. Phase transitions in machine learning. Cambridge: Cambridge University Press, 2011.
Find full textOlivier, Catoni, and Picard Jean 1959-, eds. Statistical learning theory and stochastic optimization: Ecole d'eté de probabilités de Saint-Flour XXXI, 2001. Berlin: Springer, 2004.
Find full textVapnik. Statistical Learning Theory. Wiley & Sons, Incorporated, John, 1998.
Find full textEmmert-Streib, Frank, and Matthias Dehmer. Information Theory and Statistical Learning. Springer, 2010.
Find full textVapnik, Vladimir. Nature of Statistical Learning Theory. Springer, 2013.
Find full textKulkarni, Sanjeev, and Gilbert Harman. Elementary Introduction to Statistical Learning Theory. Wiley & Sons, Incorporated, John, 2011.
Find full textThe Nature of Statistical Learning Theory. Springer, 1998.
Find full textWatanabe, Sumio. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2009.
Find full textVapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. Springer, 2013.
Find full textKulkarni, Sanjeev, and Gilbert Harman. Elementary Introduction to Statistical Learning Theory. Wiley & Sons, Incorporated, John, 2011.
Find full textKulkarni, Sanjeev, and Gilbert Harman. Elementary Introduction to Statistical Learning Theory. Wiley & Sons, Incorporated, John, 2011.
Find full textWatanabe, Sumio. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2009.
Find full textWatanabe, Sumio. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2011.
Find full textKulkarni, Sanjeev, and Gilbert Harman. Elementary Introduction to Statistical Learning Theory. Wiley & Sons, Incorporated, John, 2012.
Find full textWatanabe, Sumio. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2009.
Find full textKulkarni, Sanjeev, and Gilbert Harman. Elementary Introduction to Statistical Learning Theory. Wiley & Sons, Incorporated, John, 2011.
Find full textSumio, Watanabe. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2008.
Find full textThe nature of statistical learning theory. 2nd ed. New York: Springer, 2010.
Find full textComputational Approach to Statistical Learning. Taylor & Francis Group, 2019.
Find full textKane, Michael, Taylor Arnold, and Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.
Find full textKane, Michael, Taylor Arnold, and Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.
Find full textKane, Michael, Taylor Arnold, and Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.
Find full textKane, Michael, Taylor Arnold, and Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2019.
Find full textKane, Michael, Taylor Arnold, and Bryan W. Lewis. Computational Approach to Statistical Learning. Taylor & Francis Group, 2020.
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