Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Berlin, Germany: Springer, 2001 (Hastie T, Tibshirani R, Friedman J. 统计学习基础:数据挖掘,推理与预测.范 明,译.北京:电子工业出版社, 2004)
[2]
Jaakkola T S, Haussler D. Probabilistic Kernel Regression Models // Proc of the 7th International Workshop on Artificial Intelligence and Statistics. San Francisco, USA: Morgan Kaufmann, 1999: 99-108
[3]
Roth V. Probabilistic Discriminative Kernel Classifiers for Multi-Class Problems // Radig B, Florczyk S, eds. Lecture Notes in Computer Science. London, UK: Springer-Verlag, 2001, 2191: 246-253
[4]
Vapnik V N. The Nature of Statistical Learning Theory. New York, USA: Spinger, 1995 (Vapnik V N.统计学习理论的本质.张学工,译.北京:清华大学出版社, 2000)
[5]
Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics, 2000, 28(2): 337-407
[6]
Roth V, Steinhage V. Nonlinear Discriminant Analysis Using Kernel Functions // Solla S A, Leen T K, Muller K R, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 1999, 12: 568-574
[7]
Gao F, Klein R, Klein B, et al. Smoothing Spline ANOVA Models for Large Data Sets with Bernoulli Observations and the Randomized GACV. Annals of Statistics, 2000, 28(6):1570-1600
[8]
Smola A J, Schlkopf B. Sparse Greedy Matrix Approximation for Machine Learning // Langley P, ed. Proc of the 17th International Conference on Machine Learning. San Francisco, USA: Morgan Kaufmann, 2000: 911-918
[9]
Williams C K I, Seeger M. Using the Nystrom Method to Speed up Kernel Machines // Leen T K, Dietterich T G, Tresp V, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001, 13: 682-688
[10]
Baudat G, Anouar F. Feature Vector Selection and Projection Using Kernels. Neurocomputing, 2003, 55(1/2): 21-38
[11]
Minka T. Algorithms for Maximum-Likelihood Logistic Regression. Technical Report, 758. Pittsburgh, USA: Carnegie Mellon University. Department of Statistics, 2001
[12]
Rtsch G, Onoda T, Müller K R. Soft Margins for AdaBoost. Journal of Machine Learning, 2001, 42(3): 287-320
[13]
Hastie T, Tibshirani R. Classification by Pairwise Coupling. Annals of Statistics, 1998, 26(2): 451-471
[14]
Besag J. On the Statistical Analysis of Dirty Pictures. Journal of the Royal Statistical Society: Series B (Methodological), 1986, 48(3): 259-302
[15]
Li S Z. Markov Random Field Modeling in Computer Vision. Berlin, Germany: Springer-Verlag, 1995
[16]
Giordana N, Pieczynski W. Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(5): 465-475
[17]
Haralick R. Statistical and Structural Approaches to Texture. Proc of the IEEE, 1979, 67(5): 786-804
[18]
Berthod M, Kato Z, Shan Y, et al. Bayesian Image Classification Using Markov Random Fields. Image and Vision Computing, 1996, 14(4): 285-295
