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Application of Tunneling Effect in Quantized Probabilistic Model to Intelligent Information Processing

量化概率模型中隧道效应在智能信息处理中的应用

基本信息

批准号：
13680384
负责人：
TANAKA Kazuyuki
金额：
$ 2.3万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2002
项目状态：
已结题

项目摘要

We have constructed the hyperparameter estimation algorithms for graphical models which can be treated analytically and have estimated the statistical performances of the algorithms analytically. The research results are published as two papers in Physical Review E, vol.65, no.1 and IEICE Transactions on Information and Systems, vol.ED-65, no.3. We have introduced quantized line fields to compound Gauss-Markov random field model and have succeeded in constructing a useful algorithm for grey-level image restoration. The compound Gauss-Markov random field model is one of familiar graphical models which are powerful for image restoration and dedge etection. The compound Gauss-Markov random field model with quantized line fields can take states expressed as a superposition of edge state and no edge state. The algorithm for image restorations is constructed by using the mean-field approximation. The research results have been published as two papers in IEICE Transactions on Information and Systems, vol.J64-D-II, no.4, 2001 and Journal of Physics A, vol.35, no.37, 2002. We have reformulated a generalized belief propagation for probabilistic inference in artificial intelligence by combining a cluster variation method and a linear response theory, which are ones of familiar techniques in the statistical mechanics and applied the framework to some practical probabilistic inferences in artificial intelligence.The research results have been reported in the SICE Symposium on Systems and Information (RIKEN, Yokohama Genome Science Center, Japan, November 2002) and NIPS*2002 Workshop on Propagation Algorithm on Graphs with Cycles: Theory and Applications (Whistler, Canada, December 2002), and will appear as a paper in IEICE Transactions on Information and Systems, voi.E86-D, no.7 on July 2003.

我们构造了可解析处理的图模型的超参数估计算法，并对算法的统计性能进行了解析估计。研究结果作为两篇论文发表在Physical Review E，vol.65，no.1和IEICE Transactions on Information and Systems，vol.艾德-65，no.3上。本文将量化线场引入复合高斯-马尔可夫随机场模型，并成功地构造了一种实用的灰度图像复原算法。复合高斯-马尔可夫随机场模型是一种常见的图形模型，在图像复原和边缘检测中具有很强的应用。具有量子化线场的复合高斯-马尔可夫随机场模型可以将状态表示为边态和非边态的叠加。利用平均场近似，构造了图像简化算法。研究结果已发表在IEICE Transactions on Information and Systems，vol.J64-D-II，no.4，2001和Journal of Physics A，vol.35，no.37，2002上。结合统计力学中常见的聚类变分方法和线性响应理论，重新构造了人工智能概率推理的广义置信传播框架，并将其应用于人工智能中的一些实际概率推理，研究结果已在SICE Symposium on Systems and Information上发表（RIKEN，横滨基因组科学中心，日本，2002年11月）和NIPS*2002年关于循环图的传播算法研讨会：Theory and Applications（Whistler，Canada，2002年12月），并将于2003年7月在IEICE Transactions on Information and Systems，vol.E86-D，no.7上发表论文。