缺失标记下基于类属属性的多标记特征选择

多标记特征选择已在图像分类、疾病诊断等领域得到广泛应用;然而,现实中数据的标记空间往往存在部分标记缺失的问题,这破坏了标记间的结构性和关联性,使得学习算法难以准确地选择重要特征。针对此问题,提出一种缺失标记下基于类属属性的多标记特征选择（MFSLML）算法。首先,通过利用稀疏学习方法获取每个类标记的类属属性;同时基于线性回归模型构建类属属性与标记的映射关系,以用于恢复缺失标记;最后,选取7组数据集以及4个评价指标进行实验。实验结果表明：相比基于最大依赖度和最小冗余度的多标记特征选择算法（MDMR）和基于特征交互的多标记特征选择算法（MFML）等一些先进的多标记特征选择算法,MFSLML在平均查准率指标上能够提升4.61~5.5个百分点,由此可见MFSLML具有更优的分类性能。     

基于Fisher score与模糊邻域熵的多标记特征选择算法

针对Fisher score未充分考虑特征与标记以及标记之间的相关性，以及一些邻域粗糙集模型容易忽略边界域中知识粒的不确定性，导致算法分类性能偏低等问题，提出一种基于Fisher score与模糊邻域熵的多标记特征选择算法（MLFSF）。首先，利用最大信息系数（MIC）衡量特征与标记之间的关联程度，构建特征与标记关系矩阵；基于修正余弦相似度定义标记关系矩阵，分析标记之间的相关性。其次，给出一种二阶策略获得多个二阶标记关系组，以此重新划分多标记论域；通过增强标记之间的强相关性和削弱标记之间的弱相关性得到每个特征的得分，进而改进Fisher score模型，对多标记数据进行预处理。再次，引入多标记分类间隔，定义自适应邻域半径和邻域类并构造了上、下近似集；在此基础上提出了多标记粗糙隶属度函数，将多标记邻域粗糙集映射到模糊集，基于多标记模糊邻域给出了上、下近似集以及多标记模糊邻域粗糙集模型，由此定义模糊邻域熵和多标记模糊邻域熵，有效度量边界域的不确定性。最后，设计基于二阶标记相关性的多标记Fisher score特征选择算法（MFSLC），从而构建MLFSF。在多标记K近邻（MLKNN）分类器...     

一种基于邻域粗糙集的多标记专属特征选择方法

在多标记学习中,数据降维是一项重要且具有挑战性的任务,而特征选择又是一种高效的数据降维技术。在邻域粗糙集理论的基础上提出一种多标记专属特征选择方法,该方法从理论上确保了所得到的专属特征与相应标记具有较强的相关性,进而改善了约简效果。首先,该方法运用粗糙集理论的约简算法来减少冗余属性,在保持分类能力不变的情况下获得标记的专属特征;然后,在邻域精确度和邻域粗糙度概念的基础上,重新定义了基于邻域粗糙集的依赖度与重要度的计算方法,探讨了该模型的相关性质;最后,构建了一种基于邻域粗糙集的多标记专属特征选择模型,实现了多标记分类任务的特征选择算法。在多个公开的数据集上进行仿真实验,结果表明了该算法是有效的。     

基于标记权重的多标记特征选择算法

在多标记学习中,特征选择是解决多标记数据高维性的有效手段。每个标记对样本的可分性程度不同,这可能会为多标记学习提供一定的信息。基于这一假设,提出了一种基于标记权重的多标记特征选择算法。该算法首先利用样本在整个特征空间的分类间隔对标记进行加权,然后将特征在整个标记集合下对样本的可区分性作为特征权重,以此衡量特征对标记集合的重要性。最后,根据特征权重对特征进行降序排列,从而得到一组新的特征排序。在6个多标记数据集和4个评价指标上的实验结果表明,所提算法优于一些当前流行的多标记特征选择算法。     

基于局部子空间的多标记特征选择算法*

在已有的特征选择算法中,常用策略是通过相关准则选择与标记集合相关性较强的特征,然而该策略不一定是最优选择,因为与标记集合相关性较弱的特征可能是决定某些类别标记的关键特征.基于这一假设,文中提出基于局部子空间的多标记特征选择算法.该算法首先利用特征与标记集合之间的互信息得到一个重要度由高到低的特征序列,然后将新的特征排序空间划分为几个局部子空间,并在每个子空间设置采样比例以选择冗余性较小的特征,最后融合各子空间的特征子集,得到一组合理的特征子集.在6个数据集和4个评价指标上的实验表明,文中算法优于一些通用的多标记特征选择算法.     

基于拉普拉斯评分的多标记特征选择算法

针对传统的拉普拉斯评分特征选择算法只适应单标记学习,无法直接应用于多标记学习的问题,提出一种应用于多标记任务的拉普拉斯评分特征选择算法。首先,考虑样本在整体标记空间中共同关联和共同不关联的相关性,重新构建样本相似度矩阵;然后,将特征之间的相关性及冗余性判定引入拉普拉斯评分算法中,采用前向贪心搜索策略依次评价候选特征与已选特征的联合作用能力,用于评价特征的重要性;最后,在5个不同评价指标和6个多标记数据集上实验。实验结果表明：相比基于最大依赖的多标记维数约简方法（MDDM）、基于贝叶斯分类器的多标记特征选择算法（MLNB）及基于多元互信息的多标记分类特征选择算法（PMU）,所提算法不仅分类性能最优,且存在显著性优异达65%。     

代价敏感数据的多标记特征选择算法

在多标记学习中,特征选择是提升多标记学习分类性能的有效手段。针对多标记特征选择算法计算复杂度较大且未考虑到现实应用中数据的获取往往需要花费代价,本文提出了一种面向代价敏感数据的多标记特征选择算法。该算法利用信息熵分析特征与标记之间的相关性,重新定义了一种基于测试代价的特征重要度准则,并根据服从正态分布的特征重要度和特征代价的标准差,给出一种合理的阈值选择方法,同时通过阈值剔除冗余和不相关特征,得到低总代价的特征子集。通过在多标记数据的实验对比和分析,表明该方法的有效性和可行性。     

融合标记独有属性特征的k近邻多标记分类新算法

在多标记学习系统中,每个样本同时与多个类别标记相关,却均由一个属性特征向量描述。大部分已有的多标记分类算法采用的共同策略是使用相同的属性特征集合预测所有的类别标记,但它并非最佳选择,原因在于每个标记可能与其自身独有的属性特征相关性最大。针对这一问题,提出了融合标记独有属性特征的k近邻多标记分类算法—IML-kNN。首先对多标记数据的特征向量进行预处理,分别为每类标记构造对该类标记最具有判别能力的属性特征;然后基于得到的属性特征使用改进后的ML-kNN算法进行分类。实验结果表明,IML-kNN算法在yeast和image数据集上的性能明显优于ML-kNN算法以及其他3种常用的多标记分类算法。     

基于标记增强和模糊辨识度的标记分布特征选择

特征选择作为多标记学习任务中关键预处理步骤,能够有效地解决高维多标记数据存在的维度灾难问题。在现有大部分的多标记学习中,标记是以逻辑分布的形式刻画,即示例中相关标记的重要性相同;然而,在许多现实生活中,每个示例的标记重要程度呈现差异性。本文提出了一种基于模糊相似性的标记增强算法,通过衡量示例中标记的模糊相关性,将传统的多标记数据转换为标记分布数据;分析了标记分布数据中在标记上的标记差异性和在特征上的模糊相对辨识关系,给出了在标记空间和特征空间上的模糊辨识度,并构造了衡量特征辨识能力的特征重要度;在此基础上,构建面向标记分布数据的特征选择算法,能获得按特征重要度降序的特征选择结果。最后通过在多个多标记数据集上实验对比和分析,进一步验证了算法的有效性和可行性。     

基于双空间模糊辨识关系的多标记特征选择

已有的基于模糊粗糙集的多标记特征选择算法多从单一的样本空间刻画属性区分能力,忽视属性对标记的区分能力.基于这一认识,文中同时从样本和标记两个空间出发,提出基于双空间模糊辨识关系的多标记特征选择算法.首先,基于模糊辨识关系分别从样本和标记角度定义两种多标记属性重要性度量,然后通过权重融合的方式融合两种度量,基于融合后的度量,运用前向贪心算法构建多标记特征选择算法.在5个数据集上的对比实验验证本文算法的有效性     

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     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

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。