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back-matter - References Abu-Mostafa, Y. (1995). Hints,...

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References Abu-Mostafa, Y. (1995). Hints, Neural Computation 7 : 639–671. Ackley, D. H., Hinton, G. and Sejnowski, T. (1985). A learning algorithm for Boltzmann machines, Trends in Cognitive Sciences 9 : 147–169. Adam, B.-L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., Semmes, O. J., Schellhammer, P. F., Yasui, Y., Feng, Z. and Wright, G. (2003). Serum protein fingerprinting cou- pled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy mean, Cancer Research 63 (10): 3609–3614. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I. (1995). Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining , AAAI/MIT Press, Cambridge, MA. Agresti, A. (1996). An Introduction to Categorical Data Analysis , Wiley, New York. Agresti, A. (2002). Categorical Data Analysis (2nd Ed.) , Wiley, New York. Ahn, J. and Marron, J. (2005). The direction of maximal data piling in high dimensional space, Technical report , Statistics Department, University of North Carolina, Chapel Hill. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory , pp. 267–281.
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700 References Allen, D. (1977). The relationship between variable selection and data augmentation and a method of prediction, Technometrics 16 : 125–7. Ambroise, C. and McLachlan, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data, Proceedings of the National Academy of Sciences 99 : 6562–6566. Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees, Neural Computation 9 : 1545–1588. Anderson, J. and Rosenfeld, E. (eds) (1988). Neurocomputing: Foundations of Research , MIT Press, Cambridge, MA. Anderson, T. (2003). An Introduction to Multivariate Statistical Analysis, 3rd ed. , Wiley, New York. Bach, F. and Jordan, M. (2002). Kernel independent component analysis, Journal of Machine Learning Research 3 : 1–48. Bair, E. and Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data, PLOS Biology 2 : 511–522. Bair, E., Hastie, T., Paul, D. and Tibshirani, R. (2006). Prediction by supervised principal components, Journal of the American Statistical Association 101 : 119–137. Bakin, S. (1999). Adaptive regression and model selection in data mining problems, Technical report , PhD. thesis, Australian National Univer- sity, Canberra. Banerjee, O., Ghaoui, L. E. and d’Aspremont, A. (2008). Model selection through sparse maximum likelihood estimation for multivariate gaus- sian or binary data, Journal of Machine Learning Research 9 : 485–516. Barron, A. (1993). Universal approximation bounds for superpositions of a sigmoid function, IEEE Transactions on Information Theory 39 : 930– 945. Bartlett, P. and Traskin, M. (2007). Adaboost is consistent, in B. Sch¨ olkopf, J. Platt and T. Hoffman (eds), Advances in Neural Infor- mation Processing Systems 19 , MIT Press, Cambridge, MA, pp. 105– 112.
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