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关键词： 重要性   血液分析   血涂片  

摘要： 目的:观察在血液分析当中采取血涂片复检的临床应用价值。方法:选取2011年9月-2013年11月在我院通过血细胞分析检测标本2 645份,根据血涂片复查规则和报警信息对其中出现异常结果的标本给予人工推片、染色以及显微镜复查,给予全面分析。结果:通过血细胞分析标本2 645份,血涂片镜检测421份,其中371份血涂片镜检结构和血细胞分析仪检测结构相符合,符合率为88.12%。结论:在血液分析当中采取血涂片复检,可以避免出现漏诊,具有临床推广价值。

关键词： 包装饮用水   新国标   分类   饮用天然矿泉水   饮用纯净水   国家标准   食品安全   矿物质  

摘要： 矿泉水、矿物质水、纯净水、天然水、蒸馏水、苏打水等水品种经常让消费者有点不清楚，不过，5月24日正式实施的《食品安全国家标准包装饮用水》（GB19298—2014）（以下简称包装饮用水新国标）会让此等混乱局面变得简单不少。新国标规范统一包装饮用水分类近日，记者走访北京多家超市发现，货架上包装饮用水琳琅满目，包括饮用天然水、饮用纯净水、饮用天然矿泉水、饮用矿物质水等，此外还包括部分企业主打的蒸馏水、富氧水、苏打水、太空水、弱碱性水等等，价格则从1到10元不等，很多消费者其实并没有搞清楚这些水有什么区别。

关键词： MACHINE-LEARNING ALGORITHMS   ACCURACY   TREES  

摘要： Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classifications (including RF out-of-bag error, independent classification accuracy and class proportion error). Training data selection and specific input variables (i.e., image channels) have a large impact on the overall accuracy of the image classification. High-dimension datasets should be reduced so that only uncorrelated important variables are used in classifications. Despite the fact that RF is an ensemble approach, independent error assessments should be used to evaluate RF results, and iterative classifications are recommended to assess the stability of predicted classes. Results are also shown to be highly sensitive to the size of the training data set. In addition to being as large as possible, the training data sets used in RF classification should also be (a) randomly distributed or created in a manner that allows for the class proportions of the training data to be representative of actual class proportions in the landscape; and (b) should have minimal spatial autocorrelation to improve classification results and to mitigate inflated estimates of RF out-of-bag classification accuracy.

关键词： Multi-class data classification   Mathematical programming   Mixed integer optimisation   Hyper box representation   SUPPORT VECTOR MACHINE   MATHEMATICAL-PROGRAMMING APPROACH   DEA-DISCRIMINANT ANALYSIS   DECISION TREE CLASSIFIER   NAIVE BAYES   PATTERN-CLASSIFICATION   HYPERSPECTRAL IMAGES   GENE SELECTION   MODEL   PREDICTION  

摘要： In this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers. Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： Face recognition   Single sample per person   Generic learning   Variational Feature Representation   Face image   Normal feature   Linear regression   Non-ideal conditions   LINEAR-REGRESSION   TRAINING SAMPLE   IMAGE   ILLUMINATION   EIGENFACES   ALGORITHMS   FLDA  

摘要： The single sample per person (SSPP) problem is of great importance for real-world face recognition systems. In SSPP scenario, there is always a large gap between a normal sample enrolled in the gallery set and the non-ideal probe sample. It is a crucial step for face recognition with SSPP to bridge the gap between the ideal and non-ideal samples. For this purpose, we propose a Variational Feature Representation-based Classification (VFRC) method, which employs the linear regression model to fit the variational information of a non-ideal probe sample with respect to an ideal gallery sample. Thus, a corresponding normal feature, which reserve the identity information of the probe sample, is obtained. A combination of the normal feature and the probe sample is used, which makes VFRC method more robust and effective for SSPP scenario. The experimental results show that VFRC method possesses higher recognition rate than other related face recognition methods. (C) 2015 Elsevier Inc. All rights reserved.

关键词： High dimension low sample size classification   Action recognition   Geometric classification   Multimedia analysis  

摘要： Research on high dimension, low sample size (HDLSS) data has revealed their neighborless nature. This paper addresses the classification of HDLSS image or video data for human activity recognition. Existing approaches often use off-the-shelf classifiers such as nearest neighbor techniques or support vector machines and tend to ignore the geometry of underlying feature distributions. Addressing this issue, we investigate different geometric classifiers and affirm the lack of neighborhoods within HDLSS data. As this undermines proximity based methods and may cause over-fitting for discriminant methods, we propose a QR factorization approach to Nearest Affine Hull (NAH) classification which remedies the HDLSS dilemma and noticeably reduces time and memory requirements of existing methods. We show that the resulting non-parametric models provide smooth decision surfaces and yield efficient and accurate solutions in multiclass HDLSS scenarios. On several action recognition benchmarks, the proposed NAH classifier outperforms other instance based methods and shows competitive or superior performance than SVMs. In addition, for online settings, the proposed NAH method is faster than online SVMs. (c) 2015 Elsevier Inc. All rights reserved.

关键词： SNEAKERS   TENNIS courts   BIOMECHANICS in sports  

摘要： Click to increase image sizeClick to decrease image size

关键词： 药品检测   内霉素   凝胶法   浊度法   显色基质法  

摘要： 在药品的生产和流通过程中,内霉素污染问题是不可能完全避免的。由于细菌内霉素对人体的严重危害,药品细菌内霉素的检测程序就必不可少。现如今细菌内霉素的检测方法,主要有凝胶法、动态浊度法、显色基质法三种方法。

关键词： 双向FDI流动   国内价格波动   作用机制   协整分析   误差修正模型  

摘要： 根据开放的凯恩斯主义AD-AS模型短期均衡分析,FDI流入和流出可以通过资本项目收支、国际贸易和国内投资等渠道,从货币和实物两个方面作用于总需求和总供给,并对国内价格总水平产生影响。基于多变量的VAR模型,运用协整分析和误差修正模型方法对1982~2013年中国双向FDI流动与国内价格波动的关系进行实证检验,结果显示:(1)短期看,滞后1期CPI和国内固定资产投资对当期CPI具有较为明显的正效应;(2)FDI流入对CPI具有正向推动效应,且长期表现更加明显;(3)短期看,FDI流出和贸易开放度对CPI的影响不显著,但长期看,二者对CPI均具有显著的反向抑制效应。