基于隐含变量的聚类集成模型

聚类集成能成为机器学习活跃的研究热点,是因为聚类集成能够保护私有信息、分布式处理数据和对知识进行重用,此外,噪声和孤立点对结果的影响较小.主要工作包括:第一,分析了把每一个基聚类器看成是原数据的一个属性这种处理方式的优越性,发现按此方法建立起来的聚类集成算法就具有良好的扩展性和灵活性;第二,在此基础之上,建立了latent variable cluster ensemble(LVCE)概率模型进行聚类集成,并且给出了LVCE 模型的Markovchain Monte Carlo(MCMC)算法.实验结果表明,LVCE 模型的MCMC 算法能够进行聚类集成并且达到良好的效果,同时可以体现数据聚类的紧密程度.     

超簇加权的集成聚类算法

大多数集成聚类算法使用K-means算法生成基聚类,得到的基聚类效果不太理想.通常在使用共协矩阵对基聚类进行集成时,忽视了基聚类多样性的不同,平等地对待基聚类,且以样本为操作单元生成共协矩阵.当样本数目或集成规模较大时,计算负担显著增加.针对上述问题,提出超簇加权的集成聚类算法(ECWSC).该算法使用随机选点与K-means选点相结合来获取地标点,对地标点使用谱聚类算法得到其聚类结果,再将样本点映射到与之最近邻的地标点上生成基聚类.在此基础上,以信息熵为依据计算基聚类的不确定性,并对基聚类赋予相应权重,使用加权的方式得到加权超簇的共协矩阵,对共协矩阵使用层次聚类算法得到集成结果.选取7个真实数据集和4个人工数据集作为实验数据集,从准确度、鲁棒性和时间复杂度方面进行验证.对比实验结果表明,该算法能够有效提升集成聚类的性能.     

一种高效的双边聚类集成算法

谱聚类可以任意形状的数据进行聚类,在聚类集成中能够有效的提高基聚类的质量.以往的聚类集成算法中,聚类集成得到的结果并不是最终聚类结果,还需要利用聚类算法来获得最终聚类结果,在整个过程中会使得解由离散-连续-离散的转变.提出了一种基于谱聚类的双边聚类集成算法.算法首先在生成阶段使用谱聚类算法来获得基聚类,通过标准互信息来选取基聚类.将选出来基聚类和样本作为图的顶点,并对构建的图利用双边聚类算法对基聚类和样本同时聚类直接得到最终聚类结果.在实验中,将所提方法与一些聚类集成算法进行了比较,取得了较好的结果.     

基于数据集特点的增强聚类集成算法

当前流行的聚类集成算法无法依据不同数据集的不同特点给出恰当的处理方案,为此提出一种新的基于数据集特点的增强聚类集成算法,该算法由基聚类器的生成、基聚类器的选择与共识函数构成。该算法依据数据集的特点,通过启发式方法,选出合适的基聚类器,构建最终的基聚类器集合,并产生最终聚类结果。实验中,对ecoli,leukaemia与Vehicle三个基准数据集进行了聚类,所提出算法的聚类误差分别是0.014,0.489,0.479,同基于Bagging的结构化集成(BSEA)、异构聚类集成(HCE)和基于聚类的集成分类(COEC)算法相比,所提出算法的聚类误差始终最低;而在增加候基聚类器的情况下,所提出算法的标准化互信息(NMI)值始终高于对比算法。实验结果表明,同对比的聚类集成算法相比,所提出算法的聚类精度最高,可伸缩性最强。     

基于密度峰值的聚类集成

聚类集成的目的是为了提高聚类结果的准确性、稳定性和鲁棒性.通过集成多个基聚类结果可以产生一个较优的结果.本文提出了一个基于密度峰值的聚类集成模型,主要完成三个方面的工作: 1）在研究已有的各聚类集成算法和模型后发现各基聚类结果可以用密度表示; 2）使用改进的最大信息系数（Rapid computation of the maximal information coefficient,RapidMic）表示各基聚类结果之间的相关性,使用这种相关性来衡量原始数据在经过基聚类器聚类后相互之间的密度关系; 3）改进密度峰值（Density peaks,DP）算法进行聚类集成.最后,使用一些标准数据集对所设计的模型进行评估.实验结果表明,相比经典的聚类集成模型,本文提出的模型聚类集成效果更佳.     

Bagging组合的不平衡数据分类方法

提出一种基于Bagging组合的不平衡数据分类方法CombineBagging,采用少数类过抽样算法SMOTE进行数据预处理,在此基础上利用C-SVM、径向基函数神经网络、Random Forests 3种不同的基分类器学习算法,分别对采样后的数据样本进行Bagging集成学习,通过投票规则集成学习结果。实验结果表明,该方法能够提高少数类的分类准确率,有效处理不平衡数据分类问题。     

基于高阶一致性学习的聚类集成算法

现有的大部分关于聚类集成的研究主要关注有效的集成算法的设计。为解决由于基聚类器的质量高低不一、低质量的基聚类器对聚类集成性能产生影响的问题，从数据发掘的角度出发，以基聚类器为基础挖掘数据的内在联系，提出一种高阶信息融合算法——基于高阶一致性学习的聚类集成（HCLCE）算法，从不同的维度表示数据之间的联系。首先，将每种高阶信息融合成一个新的结构化的一致性矩阵；然后，再对得到的多个一致性矩阵进行融合；最后，将多种信息融合为一个一致性的结果。实验结果表明，与次优的LWEA(Locally Weighted Evidence Accumulation)算法相比，HCLCE算法的聚类准确率平均提升了7.22%，归一化互信息（NMI）平均提升了9.19%。可见，HCLCE能得到比聚类集成算法和单独使用一种信息更好的聚类结果。     

基于MapReduce的K-means聚类集成

针对传统聚类算法难以高效进行海量数据聚类分析的问题,提出一种基于MapReduce框架的K-means聚类集成算法。利用K-means算法生成不同聚簇数目的基聚类结果,改进共协关系矩阵,依据数据点对出现次数进行集成,自动得出最终聚类结果。实验结果表明,该算法能够有效地改善聚类质量,具有良好的扩展性,适用于海量数据的聚类分析。     

谱聚类集成的淋巴结超声图像分割算法

为了对低信噪比的超声图像进行有效分割,提出一种谱聚类集成的超声图像分割算法.首先用改进的全变差去噪模型对超声图像进行有效的去噪;然后用聚类集成的方法对去噪后的图像进行图像分割,基聚类器采用K均值算法,集成采用改进的谱聚类算法;最后用K均值算法对谱聚类集成的结果进行再次聚类,得到最终的集成聚类分割结果.实验结果表明,与现有的方法相比较,该算法分割效果更好.     

一种深度自监督聚类集成算法

针对聚类集成中一致性函数设计问题,本文提出一种深度自监督聚类集成算法。该算法首先根据基聚类划分结果采用加权连通三元组算法计算样本之间的相似度矩阵,基于相似度矩阵表达邻接关系,将基聚类由特征空间中的数据表示变换至图数据表示;在此基础上,基聚类的一致性集成问题被转化为对基聚类图数据表示的图聚类问题。为此,本文利用图神经网络构造自监督聚类集成模型,一方面采用图自动编码器学习图的低维嵌入,依据低维嵌入似然分布估计聚类集成的目标分布;另一方面利用聚类集成目标对低维嵌入过程进行指导,确保模型获得的图低维嵌入与聚类集成结果是一致最优的。在大量数据集上进行了仿真实验,结果表明本文算法相比HGPA、CSPA和MCLA等算法可以进一步提高聚类集成结果的准确性。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。