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11.8 文献笔记

原文 The Elements of Statistical Learning
翻译 szcf-weiya
发布 2017-09-06

投影寻踪由Friedman and Tukey (1974)1提出，并且由Friedman and Stuetzle (1981)2具体化为回归．Huber (1985)3给出了一个概述，并且Roosen and Hastie (1994)4采用光滑样条提出了一个关系式．神经网络的动机追溯到McCulloch and Pitts (1943)5，Widrow and Hoff (1960)6（在Anderson and Rosenfeld (1988)7中转载）以及Rosenblatt (1962)8．Hebb (1949)9严重受到学习算法发展的影响．神经网络的再起是在1980s中期，归功于Werbos (1974)10，Parker (1985)11和Rumelhart et al. (1986)12，后者提出了向后传播算法．今天这方面有很多书，Hertz et al. (1991)13，Bishop (1995)14以及Ripley (1996)15或许是信息量最大的．神经网络的贝叶斯学习在Neal (1996)16中有描述．ZIP例子取自Le Cun (1989)17，同时参见Le Cun et al. (1990)18以及Le Cun et al. (1998)19．

我们不去讨论神经网络的近似性质等理论问题，这些工作有Barron (1993)20, Girosi et al. (1995)21 和 Jones (1992)22．其中一些结果在Ripley (1996)15中进行了总结．

  1. Friedman, J. and Tukey, J. (1974). A projection pursuit algorithm for exploratory data analysis, IEEE Transactions on Computers, Series C 23: 881–889. ↩

  2. Friedman, J. and Stuetzle, W. (1981). Projection pursuit regression, Journal of the American Statistical Association 76: 817–823. ↩

  3. Huber, P. (1985). Projection pursuit, Annals of Statistics 13: 435–475. ↩

  4. Roosen, C. and Hastie, T. (1994). Automatic smoothing spline projection pursuit, Journal of Computational and Graphical Statistics 3: 235–248. ↩

  5. McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas imminent in nervous activity, Bulletin of Mathematical Biophysics 5: 115–133. Reprinted in Anderson and Rosenfeld (1988), pp 96-104. ↩

  6. Widrow, B. and Hoff, M. (1960). Adaptive switching circuits, IREWESCON Convention record, Vol. 4. pp 96-104; Reprinted in Andersen and Rosenfeld (1988). ↩

  7. Anderson, J. and Rosenfeld, E. (eds) (1988). Neurocomputing: Foundations of Research, MIT Press, Cambridge, MA. ↩

  8. Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan, Washington, D.C. ↩

  9. Hebb, D. (1949). The Organization of Behavior, Wiley, New York. ↩

  10. Werbos, P. (1974). Beyond Regression, PhD thesis, Harvard University. ↩

  11. Park, M. Y. and Hastie, T. (2007). l 1 -regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society Series B 69: 659–677. ↩

  12. Rumelhart, D., Hinton, G. and Williams, R. (1986). Learning internal representations by error propagation, in D. Rumelhart and J. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press, Cambridge, MA., pp. 318–362. ↩

  13. Hertz, J., Krogh, A. and Palmer, R. (1991). Introduction to the Theory of Neural Computation, Addison Wesley, Redwood City, CA. ↩

  14. Bishop, C. (1995). Neural Networks for Pattern Recognition, Clarendon Press, Oxford. ↩

  15. Ripley, B. D. (1996). Pattern Recognition and Neural Networks, Cambridge University Press. ↩↩

  16. Neal, R. (1996). Bayesian Learning for Neural Networks, Springer, New York. ↩

  17. Le Cun, Y. (1989). Generalization and network design strategies, Technical Report CRG-TR-89-4, Department of Computer Science, Univ. of Toronto. ↩

  18. Le Cun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard,W. and Jackel, L. (1990). Handwritten digit recognition with a back-propogation network, in D. Touretzky (ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufman, Denver, CO, pp. 386–404. ↩

  19. Le Cun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11): 2278–2324. ↩

  20. Barron, A. (1993). Universal approximation bounds for superpositions of a sigmoid function, IEEE Transactions on Information Theory 39: 930–945. ↩

  21. Girosi, F., Jones, M. and Poggio, T. (1995). Regularization theory and neural network architectures, Neural Computation 7: 219–269. ↩

  22. Jones, L. (1992). A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training, Annals of Statistics 20: 608–613. ↩

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