图像数据挖掘相关研究综述—概念和应用

图像挖掘是数据挖掘领域中新兴的领域。随着数字照相技术的发展和在多学科中的广泛应用,对大量图像数据的分析和研究越来越重要。由于图像挖掘的对象、内容不同于传统数据,方法上也不同于传统技术。本文旨在介绍图像挖掘的基本概念和体系以及国际上最新的研究成果。本文回顾了图像挖掘的相关问题及建模框架,并与模式识别、图像处理等相关领域进行了比较,在此基础上,还介绍了近年来图像挖掘领域在卫星遥感、医学影像和生物显微照片研究的相关应用。     

图像数据挖掘相关研究综述一概念和应用

图像挖掘是数据挖掘领域中新兴的领域。随着数字照相技术的发展和在多学科中的广泛应用，对大量图像数据的分析和研究越来越重要。由于图像挖掘的对象、内容不同于传统数据，方法上也不同于传统技术。本文旨在介绍图像挖掘的基本概念和体系以及国际上最新的研究成果。本文回顾了图像挖掘的相关问题及建模框架，并与模式识别、图像处理等相关领域进行了比较，在此基础上，还介绍了近年来图像挖掘领域在卫星遥感、医学影像和生物显微照片研究的相关应用。     

小样本统计决策理论：统计学习理论

统计学习理论是由Vapnik等人提出的一种有限样本统计理论,是模式识别领域新近发展的一种新理论,着重研究在小样情况下的统计规律及统计决策方法性质。它为小样本统计决策问题建立了一个较好的理论框架,也发展了一种新的通用统计决策学习方法———支持向量机,较好地解决了小样本统计决策问题。本文旨在介绍统计学习理论的基本思想、特点、研究现状和一些思考。     

基于模糊模式识别法的我国电源结构优化评价

电源结构优化是实现我国电力工业可持续发展的重要途径。从可持续发展的角度,发电系统是一个复杂庞大的系统,需要综合考虑资源、经济、环境、技术、社会等多方面因素,这也是构建完整的电源结构优化程度评价指标体系的基础。文章根据构建的评价指标体系,基于模糊模式识别理论和和方法,利用1995~2004年相关数据,对我国电源结构优化进行了评价,为电源结构优化方法和决策提供了更直观、简洁和科学、有效的判据。     

依可信度的模糊线性模式识别控制模型及应用

文章为了研究复杂信息系统模式识别控制模型,以可信性理论为依据,利用可信性测度和多目标非线性规划方法,建立了一种依可信度模糊线性模型,又根据模糊模式识别的原则,建立模糊线性模式识别控制模型.该模型具有实际的变量控制应用效果,为复杂信息系统模式识别及变量控制提供理论支持.     

基于统计特征的动态过程质量异常模式识别

文章针对目前动态过程质量异常模式的识别精度不高的问题,提出一种基于统计特征的动态过程质量异常模式识别方法.该方法首先提取出样本数据的16个统计特征,再通过相关性分析筛选出相关性较小的统计特征;然后将筛选后的相关性较小的统计特征输入支持向量机(SVM)分类器进行识别.通过仿真实验进行验证,实验结果表明,基于统计特征的异常模式识别模型能够提高整体的识别精度,可适用于生产现场的质量监控.     

小样本统计决策理论：统计学习理论

统计学习理论是由Vapnik等人提出的一种有限样本统计理论,是模式识别领域新近发展的一种新理论,着重研究在小样情况下的统计规律及统计决策方法性质。它为小样本统计决策问题建立了一个较好的理论框架,也发展了一种新的通用统计决策学习方法一一支持向量机,较好地解决了小样本统计决策问题。谊文旨在介绍统计学习理论的基本思想、特点、研究现状和一些思考。     

贝叶斯统计在句法模式识别中的应用

在计算机模式识别理论中 ,有一种重要的方法 ,即结构识别技术 ,又称句法识别技术。它的基本思想是属于分析的 ,而不是综合的。其基本原理是首先从待识别对象中萃取出特征基元 ,然后再以一系列的整合规则表示对象类中每一类模式的结构特征与性质。每一套整合规则表现为一系列的产生式 ,称为文法 ,每一类模式依据自身特有的文法能够生成许多符合自身特征的句子 ,这些句子的集合称为语言。句法识别的一般步骤是 :先将某一待识别对象或待识别模式表示成一个句子 ,然后通过句法分析 ,识别出产生该句子的文法 ,则该句子表示的模式就属于由产生的文…     

数据挖掘技术及工具的发展和应用

一、数据挖掘定义 数据挖掘是一门涉及数据库管理,人工智能,机器学习,模式识别和数据可视化的交叉学科.它是一个很难准确定义的范畴,其定义很大程度上取决于下定义者的背景和观点.     

数据挖掘中基于密度的聚类分析算法

聚类在数据挖掘、模式识别等许多领域有着重要的应用.本文介绍了聚类算法的几种分类,并例举了几种基于密度的聚类算法.最后以一种新颖的基于最大不相含核心点集的聚类算法LSNCCP为例,详细介绍整个聚类算法的工作过程.     

Spectral analysis of combustion noise and flame pattern recognition

In many situations, flame patterns in a combustion chamber cannot be observed directly by using an electronic or optical probe. However, an experienced engineer can identify the burning process by listening to the noise that it generates. In this paper, we study acoustic characteristics of turbulent impinging flames by using spectral analysis and statistical pattern recognition. By experimenting with the ignition method, different flame patterns were generated in a laboratory. We find that each flame pattern can be characterized effectively by using the power spectrum of the noise and can be identified by using this information alone.     

Review of the use of context in statistical image analysis

SUMMARY This paper is a review of the use of contextual information in statistical image analysis. After defining what we mean by 'context', we describe the Bayesian approach to high-level image analysis using deformable templates. We describe important aspects of work on character recognition and syntactic pattern recognition; in particular, aspects of the work which are relevant to scene understanding. We conclude with a review of some work on knowledge-based systems which use context to aid object recognition.     

Statistical pattern recognition in image analysis

Many tasks in image analysis can be formulated as problems of discrimination or, generally, of pattern recognition. A pattern-recognition system is normally considered to comprise two processing stages: the feature selection and extraction stage, which attempts to reduce the dimensionality of the pattern to be classified, and the classification stage, the purpose of which is to assign the pattern into its perceptually meaningful category. This paper gives an overview of the various approaches to designing statistical pattern recognition schemes. The problem of feature selection and extraction is introduced. The discussion then focuses on statistical decision theoretic rules and their implementation. Both parametric and non-parametric classification methods are covered. The emphasis then switches to decision making in context. Two basic formulations of contextual pattern classification are put forward, and the various methods developed from these two formulations are reviewed. These include the method of hidden Markov chains, the Markov random field approach, Markov meshes, and probabilistic and discrete relaxation.     

Statistical pattern recognition in image analysis

Many tasks in image analysis can be formulated as problems of discrimination or, generally, of pattern recognition. A pattern-recognition system is normally considered to comprise two processing stages: the feature selection and extraction stage, which attempts to reduce the dimensionality of the pattern to be classified, and the classification stage, the purpose of which is to assign the pattern into its perceptually meaningful category. This paper gives an overview of the various approaches to designing statistical pattern recognition schemes. The problem of feature selection and extraction is introduced. The discussion then focuses on statistical decision theoretic rules and their implementation. Both parametric and non-parametric classification methods are covered. The emphasis then switches to decision making in context. Two basic formulations of contextual pattern classification are put forward, and the various methods developed from these two formulations are reviewed. These include the method of hidden Markov chains, the Markov random field approach, Markov meshes, and probabilistic and discrete relaxation.     

Investigating the role of orthogonal and non – orthogonal rotation in multivariate factor analysis,in regard to the repeatability of the extracted factors: A simulation study

ABSTRACT

Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The main goal is to test the reliability of the results derived through FA and to reveal the best rotation method under various scenarios. Based on the results of the simulations, it was observed that when applying non-orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, and, when no rotation was applied.     

Boltzmann machines that learn to recognize patterns on control charts

Boltzmann machines (BM), a type of neural networking algorithm, have been proven to be useful in pattern recognition. Patterns on quality control charts have long been recognized as providing useful information for correcting process performance problems. In computer-integrated manufacturing environments, where the control charts are monitored by computer algorithms, the potential for using pattern-recognition algorithms is considerable. The main purpose of this paper is to formulate a Boltzmann machine pattern recognizer (BMPR) and demonstrate its utility in control chart pattern recognition. It is not the intent of this paper to make comparisons between existing related algorithms. A factorial design of experiments was conducted to study the effects of numerous factors on the convergence behavior and performance of these BMPRs. These factors include the number of hidden nodes used in the network and the annealing schedule. Simulations indicate that the temperature level of the annealing schedule significantly affects the convergence behavior of the training process and that, to achieve a balanced performance of these BMPRs, a medium to high level of annealing temperatures is recommended. Numerical results for cyclical and stratification patterns illustrate that the classification capability of these BMPRs is quite powerful.     

数字知识经济增加值核算研究

数字知识经济作为数字经济的重要组成部分,随着各类互联网平台的发展得到了飞速发展,研究数字知识经济核算问题是完成我国数字经济核算的重要一环。本文在对数字知识经济内涵、范围与分类研究的基础上,对数字知识经济的生产模式、收益模式以及与平台的合作模式进行了归纳;进而根据平台所起的作用,分别以数字知识平台与生产者作为核算主体,详细研究各种模式下的总产出与中间消耗,确定生产法增加值核算方案。根据生产者是个人还是企业、平台是否参与生产等因素,详细讨论各种情况下劳动报酬、营业盈余、混合收入、生产税净额、固定资本消耗的核算内容,确定收入法增加值的核算方案;并探究支出法GDP核算中数字知识经济的核算项目。以“得到”这一典型企业为例,采用生产法和收入法核算了其2020年1月至6月的增加值,并分析两种方法存在差异的原因和适用性,为有关部门进行数字知识经济增加值的核算提供参考依据。     

An invitation to quantum tomography

Summary.  We describe quantum tomography as an inverse statistical problem in which the quantum state of a light beam is the unknown parameter and the data are given by results of measurements performed on identical quantum systems. The state can be represented as an infinite dimensional density matrix or equivalently as a density on the plane called the Wigner function. We present consistency results for pattern function projection estimators and for sieve maximum likelihood estimators for both the density matrix of the quantum state and its Wigner function. We illustrate the performance of the estimators on simulated data. An EM algorithm is proposed for practical implementation. There remain many open problems, e.g. rates of convergence, adaptation and studying other estimators; a main purpose of the paper is to bring these to the attention of the statistical community.     

Parameter estimation for a deformable template model

In recent years, a number of statistical models have been proposed for the purposes of high-level image analysis tasks such as object recognition. However, in general, these models remain hard to use in practice, partly as a result of their complexity, partly through lack of software. In this paper we concentrate on a particular deformable template model which has proved potentially useful for locating and labelling cells in microscope slides Rue and Hurn (1999). This model requires the specification of a number of rather non-intuitive parameters which control the shape variability of the deformed templates. Our goal is to arrange the estimation of these parameters in such a way that the microscope user's expertise is exploited to provide the necessary training data graphically by identifying a number of cells displayed on a computer screen, but that no additional statistical input is required. In this paper we use maximum likelihood estimation incorporating the error structure in the generation of our training data.