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关键词： K-means   Support vector machines (SVMs)   Close-to-cluster-border sampling   Near-cluster-center sampling   Reduced convex hulls (RCHs)   Spectral angular mapper (SAM)   Remote sensing images  

摘要： The definition of valuable training samples and automatic classification of land cover with remote sensing data are both classical problems, which are known to be difficult and have attracted major research efforts. In this paper, a method of modified K-means-based support vector machine (SVM) classification is proposed to use a hybrid sample selection that leverages the informativeness and representativeness of training samples to classify real multi/hyperspectral images. The hybrid sample selection (close-to-cluster-border sampling and near-cluster-center sampling) is constructed on the reduced convex hulls (RCHs) of clustering structure and can reduce the risk of overtraining caused by active sample selection of active learning methods. Numerical results obtained on the classification of three challenging remote sensing images (Landsat-7 ETM+, AVIRIS Indian pines, and KSC) by comparing the proposed technique with random sampling (RS) and margin sampling (MS) demonstrate the good efficiency and high accuracy of our approach.

关键词： Positive ions  

摘要： It is important to understand the electrolyte-electrode interactions for fabricating graphene oxide (GO)- and ionic liquid (IL)-based ultracapacitors. Therefore, we explored how the type and size of the cations in various ILs determine the nature of processed materials. In all cases, the ILs intercalate into the graphitic structure but marked differences are observed during exfoliation via thermal reduction. The combination of a long alkyl chain ammonium-based cation and a large-volume anion leads to strong interactions and defect formation, as evidenced by CO2 production during annealing. In contrast, using the same anions but different cations stabilize the GO functional groups below 400 degrees C.

关键词： Cognitive style   Computer modeling tools   Problem solving   Complex problem  

摘要： An investigation was carried out to examine the effects of cognitive style on learners' performance and interaction during complex problem solving with a computer modeling tool. One hundred and nineteen undergraduates volunteered to participate in the study. Participants were first administered a test, and based on their test scores they were classified into three groups, namely field-dependent, field-mixed, and field-independent learners. Participants then received the same set of integrated-format materials and were asked to use a computer modeling tool to solve a complex problem about immigration policy. A multivariate analysis of variance was performed with field type as the independent variable, and cognitive load, problem-solving performance, and learner interaction with the computer tool as the dependent variables. The results indicated that there was no significant difference in terms of the amount of cognitive load reported. However, there was a significant difference in terms of learner problem-solving performance. Specifically, field-independent learners outperformed field-dependent learners, and field-mixed learners outperformed field-dependent learners. The results also indicated significant differences in computer interaction between field-independent and field-dependent learners, and between field-mixed and field-dependent learners. The qualitative findings of the study showed that students who interacted poorly with the software were unsure about how to systematically use the affordances of the computer tool to solve the problem, did not have a goal-directed plan or strategy in mind about how to investigate the issue at hand, and had difficulty with testing the immigration policies by appropriately controlling variables in order to collect data to inform decision making. Implications are discussed in terms of designing computer systems that scaffold learners' complex problem solving by considering the cognitive demands of the task. (C) 2012 Elsevier

关键词： Synchronization   Localization   Time of arrival (TOA)   Clock bias   Wireless sensor networks  

摘要： This paper considers the joint node synchronization and localization problem, a key enabling aspect for wireless sensor networks. A new algorithm that can jointly estimate the clock bias and the position of an unknown sensor node is developed. The newly proposed method only involves the applications of closed-form weighted least squares (WLS) technique and therefore has lower computational complexity than conventional maximum likelihood (ML) estimators. More importantly, through theoretical performance analysis, the new algorithm is shown to be able to achieve the Cramer-Rao Lower Bound (CRLB) accuracy under the condition of high signal-to-noise ratio (SNR), a prominent advantage over other existing closed-form methods. Simulations demonstrate the good performance of the developed algorithm. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Supervised learning   Error estimation   High dimension   Small sample setting   Confidence interval  

摘要： Error estimation accuracy is the salient issue regarding the validity of a classifier model. When samples are small, training-data-based error estimates tend to suffer from inaccuracy and quantification of error estimation accuracy is difficult. Numerous methods have been proposed for estimating confidence intervals for the true error based on the estimated error. This paper surveys proposed methods and quantifies their performance. Monte Carlo methods are used to obtain accurate estimates of the true confidence intervals and compare these to the intervals estimated from samples. We consider different error estimators and several proposed confidence-bound estimators. Both synthetic and real genomic data are employed. Our simulations show the majority of the confidence intervals methods have poor performance because of the difference of shape between true and estimated intervals. According to our results, the best estimation strategy is to use the 10-time 10-fold cross-validation with a confidence interval based on the standard deviation. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Two-factor interaction   rank test   power  

ISBN: (数字)9780735411838

摘要： The null hypothesis of no interaction in a two-way cross-classification model with non-normal errors is tested using a test statistic based on a nonlinear function of the ranks of the residuals in the model under the null hypothesis. When the errors are normal and the number of replicates is still fairly small, the proposed rank test is found to have powers which are comparable to those of the usual F test.

关键词： 驻锚货币   区域性货币锚   发展路径  

摘要： 本文针对人民币跨境流通使用后出现的结算货币与计价货币分离、人民币计价职能难以发挥等问题,从驻锚货币的角度进行了讨论和研究,认为成为货币锚是货币广泛发挥国际计价功能的集中体现和关键基础,也是一国货币国际化成败的最终标志。为此,本文构建了货币替代模型,研究人民币形成货币锚的初始选择,并在此基础上提出了相应发展路径。

关键词： 债券市场   违约处理   人大   侧记   个人投资者   投资群体   可能性   公司债  

摘要： 虽然这并不是中国债市上首次债券持有人大会．却是继”福禧事件”后最受市场关注的一场债券持有人大会，不仅因为“11超日债”被视为可能性最高的债券市场”违约第一单”，还因为这只公司债的投资群体包括众多的个人投资者。

关键词： 竞业限制协议   用人单位   责任竞合   违约责任   实务处理   违反   劳动者   诉权  

摘要： 一、问题的提出 周某于2008年3月进入A测试公司从事销售工作。双方签订了商业秘密保护及竞业限制协议，对周某的竞业限制义务作了约定，并约定：周某违反竞业禁止条款的，所得全部收益应当归A测试公司所有，并根据其从事竞业的自然月数，按照每月双倍于其在A测试公司任职期间月工资的标准向A测试公司支付违约金：违约金不足以弥补A测试公司损失的，A测试公司有权进一步追偿。

关键词： 贫血   血液检验   鉴别诊断  

摘要： 贫血在临床上是一种常见的症状，其定义是在单位容积的循环血液中，红细胞的数量、红细胞比积与血红蛋白量均小于正常值，并且伴随全血容量的减少，导致出现综合的症状。各个系统的疾病都有可能导致贫血。临床上，营养不良性的贫血主要包括缺铁性与巨幼细胞性2种类型，而巨幼红细胞性是最为常见的一种营养不良性贫血，经常危及患者的发育及身体的健康。