后验概率估计及其应用:基于核Logistic回归的方法*

李滔,王俊普,吴秀清,唐金辉

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模式识别与人工智能 ›› 2006, Vol. 19 ›› Issue (6) : 689-695.
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后验概率估计及其应用:基于核Logistic回归的方法*

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Estimation of Posterior Probability and Applications: An Approach Based on Kernel Logistic Regression

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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.

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.

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

Key words

Estimation of Posteriori Probability / Kernel Logistic Regression / Feature Vector Selection / Markov Random Field / Image Segmentation

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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*. 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. Pattern Recognition and Artificial Intelligence. 2006, 19(6): 689-695

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基金

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