References
1 Ripley, BD. %22Classification‐II%22. In: Kotz, S, Balakrishnan, N, Read, C, Vidakovic, B, eds. Encyclopedia of Statistical Sciences. vol 2. New York:
John Wiley %26 Sons; 2006, 975–981.
2 Le Cun, Y, Boser, B, Denker, JS, Henderson, D, Howard, RE,
et al. Backpropagation applied to handwritten Zip code recognition. Neural Comp 1989, 1: 541–551.
3 Geman, S, Bienenstock, E, Doursat, R. Neural networks and the bias/variance dilemma. Neural Comp 1992, 4: 1–58.
4 Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ. Classification and Regression Trees. Belmont, CA:
Wadsworth; 1984.
5 Friedman, JH. Multivariate adaptive regression splines (with discussion). Ann Stat 1991, 19: 1–141.
6 Ripley, BD. Pattern Recognition and Neural Networks. Cambridge:
Cambridge University Press; 2006.
7 Connor, J, Atlas, LE, Martin, DR. %22Recurrent networks and NARMA modeling%22. In: Moody, J, Hanson, S, Lippmann, R, eds. Advances in Neural Information Processing Systems. vol. 4. San Mateo, CA:
Morgan Kaufmann; 1992, 301–308.
8 Hopfield, JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 1982, 80: 3088–3092.
9 Hertz, L, Krogh, A, Palmer, RG. Introduction to the Theory of Neural Computation. Redwood City, CA:
Addison‐Wesley; 1991.
10 Aarts, EHL, Korst, JHM. Simulated Annealing and Boltzmann Machines. Chichester:
John Wiley %26 Sons; 1989.
11 Lippmann, RP. An introduction to computing with neural nets. IEEE Trans Acoust Speech Sig Process 1987, 4: 4–22.
12 Cheng, B, Titterington, DM. Neural networks: a review from a statistical perspective (with discussion). Stat Sci 1994, 9: 2–54.
13 Kohonen, T. Self‐organization and Associative Memory.
3rd ed. Berlin:
Springer; 1989.
14 Rumelhart, DE, Zipser, D. Feature discovery by competitive learning. Cogn Sci 1985, 9: 75–112.
15 Carpenter, GA, Grossberg, S. The ART of adaptive pattern recognition by a self‐organizing neural network. Computer 1988, 21: 77–88.
16 Buntine, W, Weigend, A. Bayesian backpropagation. Complex Syst 1991, 5: 603–643.
17 MacKay, DJC. A practical Bayesian framework for backpropagation networks. Neural Comp 1992, 4: 448–472.
18 Paige, RL, Butler, RW. Bayesian inference in neural networks. Biometrika 2001, 88: 623–641.
19 Lee, HKH. Model selection for neural network classification. Class J 2001, 18: 227–243.
20 Neal, RM. Bayesian Learning for Neural Networks. Cambridge, MA:
MIT Press; 1996.
21 MacKay, DJC. Probable networks and plausible predictions ‐ a review of practical Bayesian methods for supervised neural networks. Networks: Comp Neural Syst 1995, 6: 469–505.
22 Titterington, DM. Bayesian methods for neural networks and related models. Stat Sci 2004, 19: 128–139.
23 Green, PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995, 82: 711–732.
24 Hinton, GE, van Camp, D. %22Keeping neural networks simple by minimizing the description length of the weights%22. Proceedings of the 6th ACM Conference on Computational Learning Theory. New York:
ACM Press; 1993, 5–13.
25 Barber, D, Bishop, CM. %22Ensemble learning in Bayesian neural networks%22. In: Bishop, CM, eds. Neural Networks and Machine Learning. New York:
Springer; 1998, 215–237.
26 Lee, HKH. Bayesian Nonparametrics via Neural Networks. Philadelphia, PA:
ASA‐SIAM; 2004.
27 McClelland, JL. Discussion of 12. Stat Sci 1994, 9: 42–45.
28 Shawe‐Taylor, J, Cristianini, N. Support Vector Machines and Other Kernel‐based Learning Methods:
Cambridge University Press; 2000.
29 Lauritzen, SL. Graphical Models. Oxford:
Clarendon Press; 1966.
30 Jordan, MI, (ed.) Learning in Graphical Models. Cambridge, MA:
MIT Press; 1999.
31 Rasmussen, CE, Williams, CKI. Gaussian Processes for Machine Learning. Cambridge, MA:
MIT press; 2006.
32 Hastie, T, Tibshirani, R, Friedman, J. The Elements of Statistical Learning. New York:
Springer; 2001.
33 Bishop, CM. Pattern Recognition and Machine Learning. New York:
Springer; 2006.