后验概率估计及其应用:基于核Logistic回归的方法<sup>*</sup>

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1124 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要提出一种基于特征矢量集的核Logistic回归方法,解决核Logistic回归的解的稀疏性问题,降低后验概率估计的计算复杂度.该方法与Markov随机场方法相结合,应用到图像分割中.在Bayes公式中,对样本条件概率的估计转换为对核Logistic回归方法的后验概率的估计,从而提出一种新的Markov随机场模型的实现方法,在对纹理图像的分割实验中得到良好效果.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李滔
	王俊普
	吴秀清
	唐金辉

关键词 ：后验概率估计, 核Logistic回归, 特征矢量选择, Markov随机场, 图像分割

Abstract：A method based on feature vector set is proposed to render a sparse solution for kernel Logistic Regression (LR) and decrease computation complexity of posterior probability estimation. The proposed method is combined with Markov Random Field (MRF) in terms of Bayes rule, in which the conditional probability is replaced with posterior probability estimated by kernel LR. The combination is applied to image segmentation. Experiments on texture image segmentation show the performance of the proposed method is suporior to that of Gaussian MRF method.

Key words： Estimation of Posteriori Probability Kernel Logistic Regression Feature Vector Selection Markov Random Field Image Segmentation

收稿日期: 2005-07-18

ZTFLH:

TP181

基金资助:国家863项目(No.2004AA783052)、中国科学院知识创新项目(No.KZCX0101)资助

作者简介: 李滔,男,1977年生,博士研究生,主要研究方向为机器学习、模式识别、图像处理.E-mail: leet@ustc.edu.王俊普,男,1940年生,教授,博士生导师,主要研究方向为智能控制、人工智能、机器学习、智能系统等.吴秀清,女,1944年生,教授,博士生导师,主要研究方向为图像融合、智能信息处理.唐金辉,男,1981年生,博士研究生,主要研究方向为图像处理.

引用本文:

李滔，王俊普，吴秀清，唐金辉. 后验概率估计及其应用:基于核Logistic回归的方法^*[J]. 模式识别与人工智能, 2006, 19(6): 689-695. LI Tao, WANG JunPu, WU XiuQing, TANG JinHui. Estimation of Posterior Probability and Applications: An Approach Based on Kernel Logistic Regression. , 2006, 19(6): 689-695.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2006/V19/I6/689

[1] 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, Schlkopf 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]Rtsch 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