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.

我们已经为图形模型构建了高参数估计算法，这些算法可以在分析上进行处理，并通过分析算法估算了算法的统计性能。研究结果发表在物理评论E，第1卷，第1卷和IECE信息和系统的IEICE交易中，第3卷，第3期。我们已经将量化的线场引入了复合高斯随机场模型，并成功地构建了用于灰色级图像恢复的有用算法。复合高斯 - 马尔科夫随机场模型是一种熟悉的图形模型之一，可用于图像恢复和凹槽。具有量化线场的复合高斯 - 马尔科夫随机场模型可以将表达为边缘状态叠加和没有边缘状态的状态。图像还原算法是通过使用平均场近似构建的。该研究结果已作为信息和系统的IEICE交易中的两篇论文发表，第J64-D-II，第4卷，2001年和《物理学杂志》和《物理A》，第35卷，第37期，2002年。我们已经重新制定了一种概率的概率推断，用于在人工机构中重新制定人工智能的统计量和型号的机构，该机构熟悉的技术机构，该技术的技术机构，该技术是一种熟悉的技术，该技术是一种熟悉的技术，是该技术的技术，该技术是众所周知的技术，该技术是众所料在人工智能中的实践概率推论。研究结果已在系统和信息的SICE研讨会上报道（Riken，Riken，横滨基因组科学中心，日本，2002年11月，2002年11月）*2002年*2002年的nips*2002传播算法的讲习班与周期的图表上的传播算法讲习班VOI.E86-D，第7号，2003年7月。