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基于剪辑最近邻法的股市趋势的模式识别分类研究 被引量:2
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作者 王利 周美娇 +2 位作者 张凤登 念蓓 曹晓璇 《微计算机信息》 2010年第34期237-239,233,共4页
本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪... 本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,选股及时,具有较高的针对性,实用性较强,并可进一步改进。 展开更多
关键词 剪辑最近邻法 股市趋势 模式识别分类
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基于K-近邻法的股价曲线的模式识别分类研究 被引量:1
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作者 王利 周美娇 +2 位作者 张凤登 念蓓 曹晓璇 《微计算机信息》 2011年第1期281-284,共4页
本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪... 本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,具有良好的实时性和较高的针对性,实用性较强,并可进一步改进。 展开更多
关键词 K-近邻法 股价曲线 模式识别分类
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基于最近邻法的股市趋势的模式识别分类研究
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作者 王利 周美娇 +2 位作者 张凤登 念蓓 曹晓璇 《微计算机信息》 2010年第31期90-92,64,共4页
本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪... 本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,具有良好的实时性和较高的针对性,实用性较强,并可进一步改进。 展开更多
关键词 最近邻法 股市趋势 模式识别分类
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谈模式识别与分类技术及金融业的“知识经济
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作者 刘中华 《中国金融电脑》 1998年第9期54-56,共3页
一、从数据富矿中淘金人类的特点之一是能应用概念进行逻辑推理和判断,然而,在现代市场经济和“信息爆炸”的年代,经济和金融界的分析人员往往需要面对大量的原始数据,这些原始数据常常不是以文字或图形表示得比较清晰的概念,而是... 一、从数据富矿中淘金人类的特点之一是能应用概念进行逻辑推理和判断,然而,在现代市场经济和“信息爆炸”的年代,经济和金融界的分析人员往往需要面对大量的原始数据,这些原始数据常常不是以文字或图形表示得比较清晰的概念,而是让人望而生畏的的数字集合。要从这类... 展开更多
关键词 模式识别 分类技术 金融业 模式分类 数据挖掘技术 数据仓库 知识经 模式识别分类 建立模型 西方商业银行
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基于流形距离的人工免疫无监督分类与识别算法 被引量:30
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作者 公茂果 焦李成 +1 位作者 马文萍 张向荣 《自动化学报》 EI CSCD 北大核心 2008年第3期367-375,共9页
将一种新的流形距离作为相似性度量测度,提出了一种用于无监督分类与识别的人工免疫系统方法.通过基于流形距离的相似性度量,有效利用样本集固有的全局一致性信息,充分挖掘无类属样本的空间分布信息,对样本进行类别划分.新方法将免疫响... 将一种新的流形距离作为相似性度量测度,提出了一种用于无监督分类与识别的人工免疫系统方法.通过基于流形距离的相似性度量,有效利用样本集固有的全局一致性信息,充分挖掘无类属样本的空间分布信息,对样本进行类别划分.新方法将免疫响应过程建模为一个四元组AIR=(G,I,R,A),其中G为引发免疫响应的外界刺激,即抗原;I为所有可能抗体的集合;R为抗体间相互作用的规则集合;A为支配抗体反应、指导抗体进化的动态算法.针对无监督分类问题,将抗体编码为代表各类别的典型样本序号的排列,利用动态算法A搜索能代表各类别的典型样本的最佳组合.将新方法与标准的K-均值算法、基于流形距离的进化聚类算法以及Maulik等人提出的基于遗传算法的聚类算法进行了性能比较.对6个人工数据集及手写体数字识别问题的仿真实验结果显示,新方法对样本空间分布复杂的无监督分类问题和实际的模式识别问题具有较高的准确率和较好的鲁棒性. 展开更多
关键词 人工免疫系统 流形 无监督分类 聚类 模式识别
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基于判别分类和回归计算的传感器节点定位 被引量:2
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作者 朱东进 王婷 《无线电工程》 北大核心 2021年第11期1289-1295,共7页
为了实现自组织传感器网络中传感器节点的定位,提出了一种无需复杂测距过程基于判别分类和回归计算的定位算法。在训练阶段,选择一个合适的判别函数来使用任意构造的边界对节点位置进行分类识别,以获得位置未知节点的位置的粗粒度估计,... 为了实现自组织传感器网络中传感器节点的定位,提出了一种无需复杂测距过程基于判别分类和回归计算的定位算法。在训练阶段,选择一个合适的判别函数来使用任意构造的边界对节点位置进行分类识别,以获得位置未知节点的位置的粗粒度估计,该阶段可在少量具有足够的能量和处理能力的位置已知的传感器节点上在线或离线进行;训练阶段结束,其他位置未知的大量低功率传感器节点可以通过回归计算来局部确定自己的位置,从而实现细粒度定位。仿真实验结果表明,所提算法不仅与网络的拓扑无关,而且相比于过程复杂的测距定位算法具有更小的定位误差。 展开更多
关键词 无线传感器网络 节点定位 模式识别分类 回归计算 核函数 特征空间向量 定位误差
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中医脉诊客观化与数字化研究 被引量:13
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作者 余伶俐 《辽宁中医杂志》 CAS 北大核心 2006年第2期129-131,共3页
要实现中医脉诊数字化,就必须有符合中医特点的脉搏仪及相应软件配合,更要有中医特色的脉象信息分析方法和识别技术。中医脉诊临床研究为古代客观化与现代数字化研究提供了有效参考数据,脉诊机理的探讨为其可行性提供了有利依据。从脉... 要实现中医脉诊数字化,就必须有符合中医特点的脉搏仪及相应软件配合,更要有中医特色的脉象信息分析方法和识别技术。中医脉诊临床研究为古代客观化与现代数字化研究提供了有效参考数据,脉诊机理的探讨为其可行性提供了有利依据。从脉象信号特征提取和脉象信号模式识别与分类两个角度综述了20年脉象信号分析方法的研究现状,并对存在的问题和进一步研究方法进行剖析。 展开更多
关键词 脉象 中医数字化 信号分析 模式识别分类
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Identification and Quantification of Non-Spherical Particles 被引量:1
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作者 曾周末 张宝明 杨庆 《Transactions of Tianjin University》 EI CAS 2002年第2期75-78,共4页
In commercial applications of phase Doppler anemometry (PDA), the effectiveness of non sphericity of particles is present and the response of PDA system deviates from the theoretical prediction. In this paper, the st... In commercial applications of phase Doppler anemometry (PDA), the effectiveness of non sphericity of particles is present and the response of PDA system deviates from the theoretical prediction. In this paper, the statistic characteristics of PDA signal related to irregular particles is analyzed and a method of statistic classification of irregular particles is proposed.It proves that the parameter of PDA signal for irregular particles is an unbiased estimation for spherical ones, the mean of the phase difference is in direct proportion to the mean diameter of particles and the standard deviation of the phase difference increases linearly with the standard deviation of irregular particles. As an application of the identification of irregular objects, fuzzy patterns and similarities of haemocytes are used to recognize and quantify cell samples.The statistic classification of particles is more significant in practice. 展开更多
关键词 particle measurement phase Doppler anemometry statistic classification pattern recognition
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Early-stage Internet traffic identification based on packet payload size
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作者 吴同 韩臻 +1 位作者 王伟 彭立志 《Journal of Southeast University(English Edition)》 EI CAS 2014年第3期289-295,共7页
In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets w... In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification. 展开更多
关键词 pattern recognition network measurement traffic classification traffic feature
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DIMENSIONALITY REDUCTION BASED ON SVM AND LDA,AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE 被引量:1
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作者 杨波 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2009年第4期306-312,共7页
Some dimensionality reduction (DR) approaches based on support vector machine (SVM) are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on S... Some dimensionality reduction (DR) approaches based on support vector machine (SVM) are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA (DRSL). DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method. 展开更多
关键词 classification information pattern recognition dimensionality reduction (DR) support vectormachine (SVM) linear discriminant analysis (LDA)
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DISCRIMINATIVE REGULARIZATION:A NEW CLASSIFIER LEARNING METHOD
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作者 薛晖 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2009年第1期65-74,共10页
A novel regularization method -- discriminative regularization (DR)is presented. The method provides a general way to incorporate the prior knowledge for the classification. By introducing the prior information into... A novel regularization method -- discriminative regularization (DR)is presented. The method provides a general way to incorporate the prior knowledge for the classification. By introducing the prior information into the regularization term, DR is used to minimize the empirical loss between the desired and actual outputs, as well as maximize the inter-class separability and minimize the intra-class compactness in the output space simultane- ously. Furthermore, by embedding equality constraints in the formulation, the solution of DR can solve a set of linear equations. Classification experiments show the superiority of the proposed DR. 展开更多
关键词 discriminant analysis classification of information pattern recognition
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基于改进ReliefF算法的特征加权FCM 被引量:1
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作者 张鸿 《舰船电子对抗》 2012年第1期79-82,85,共5页
为改善模糊C均值(FCM)聚类分析算法的性能,减少FCM聚类算法的误分率,提高FCM聚类算法的稳定性,提出了一种改进ReliefF加权FCM(IReliefF-WFCM)聚类算法。IReliefF算法改进了传统ReliefF算法的样本点选择方法,得到了更加稳定有效的特征权... 为改善模糊C均值(FCM)聚类分析算法的性能,减少FCM聚类算法的误分率,提高FCM聚类算法的稳定性,提出了一种改进ReliefF加权FCM(IReliefF-WFCM)聚类算法。IReliefF算法改进了传统ReliefF算法的样本点选择方法,得到了更加稳定有效的特征权值。最后,将该IReliefF-WFCM算法用于数据集等实际数据的聚类分析。结果表明该方法是可行、有效的,为分类模式识别提供了一种误分率小的、稳定的方法。 展开更多
关键词 模糊C均值 聚类算法 特征加权 分类模式识别
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Noise-assisted MEMD based relevant IMFs identification and EEG classification 被引量:5
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作者 SHE Qing-shan MA Yu-liang +2 位作者 MENG Ming XI Xu-gang LUO Zhi-zeng 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第3期599-608,共10页
Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provi... Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets. 展开更多
关键词 multichannel electroencephalography noise-assisted multivariate empirical mode decomposition Jensen-Shannondistance brain-computer interface
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TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION 被引量:8
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作者 Zhang Xinsheng Gao Xinbo Wang Ying 《Journal of Electronics(China)》 2009年第3期318-325,共8页
Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonab... Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem. 展开更多
关键词 Microcalcification Clusters (MCs) detection TWin Support Tensor Machine (TWSTM) TWin Support Vector Machine (TWSVM) Receiver Operating Characteristic (ROC) curve
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OBLIQUE PROJECTION REALIZATION OF A KERNEL-BASED NONLINEAR DISCRIMINATOR
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作者 Liu Benyong Zhang Jing 《Journal of Electronics(China)》 2006年第1期94-98,共5页
Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the t... Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the target class, this paper introduces an oblique projection algorithm to determine the coefficients of a KND so that it is extended to a new version called extended KND (eKND). In eKND construction, the desired output vector of the target class is obliquely projected onto the relevant subspace along the subspace related to other classes. In addition, a simple technique is proposed to calculate the associated oblique projection operator. Experimental results on handwritten digit recognition show that the algorithm performes better than a KND classifier and some other commonly used classifiers. 展开更多
关键词 Pattern recognition Nonlinear classifier Kernel-based Nonlinear Discriminator(KND) Extended KND(eKND) Handwritten digit recognition
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航空发动机控制系统执行机构参数在线估计方法 被引量:1
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作者 季春生 王元 卢俊杰 《航空动力学报》 EI CAS CSCD 北大核心 2024年第8期393-403,共11页
针对航空发动机控制系统因为执行机构性能退化而导致发动机控制品质降低或者严重时威胁发动机运行安全的状况,开展执行机构状态参数在线估计方法研究。在航空发动机实际执行机构控制回路实测信号较少的情况下,提出一种组合参数在线估计... 针对航空发动机控制系统因为执行机构性能退化而导致发动机控制品质降低或者严重时威胁发动机运行安全的状况,开展执行机构状态参数在线估计方法研究。在航空发动机实际执行机构控制回路实测信号较少的情况下,提出一种组合参数在线估计方法,通过作动模式识别分类,基于无迹卡尔曼滤波以稳态模式输出估计电液伺服阀平衡电流,基于拟牛顿算法(BFGS)以动态模式输出估计执行机构增益和作动延迟时间,实现模型参数的在线更新,建立实时自适应执行机构模型。以某涡扇发动机导叶作动控制回路为对象进行仿真,结果表明:在只有作动位置单一参数可测的条件下,在不同作动状态下对执行机构控制回路的平衡电流估计误差优于±0.2 mA,执行机构增益估计误差优于±4%,作动延迟周期估计误差不超过1个控制周期,能够实时跟踪并较为准确地估计执行机构的工作状态,为航空发动机执行机构控制回路设计与故障诊断提供技术支撑。 展开更多
关键词 航空发动机控制系统 执行机构 自适应模型 参数估计 模式识别分类 状态跟踪
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Hyperbolic Tangent Support Vector Machine
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作者 刘叶青 刘三阳 谷明涛 《Journal of Donghua University(English Edition)》 EI CAS 2010年第5期705-708,共4页
By utilizing hyperbolic tangent function,we constructed a novel hyperbolic tangent loss function to reduce the influences of outliers on support vector machine(SVM)classification problem.The new loss function not only... By utilizing hyperbolic tangent function,we constructed a novel hyperbolic tangent loss function to reduce the influences of outliers on support vector machine(SVM)classification problem.The new loss function not only limits the maximal loss value of outliers but also is smooth.Hyperbolic tangent SVM(HTSVM)is then proposed based on the new loss function.The experimental results show that HTSVM reduces the effects of outliers and gives better generalization performance than the classical SVM on both artificial data and UCI data sets.Therefore,the proposed hyperbolic tangent loss function and HTSVM are both effective. 展开更多
关键词 support vector machine(SVM) CLASSIFICATION pattern recognition
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ASYMBOOST-BASED FISHER LINEAR CLASSIFIER FOR FACE RECOGNITION
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作者 Wang Xianji Ye Xueyi Li Bin Li Xin Zhuang Zhenquan 《Journal of Electronics(China)》 2008年第3期352-357,共6页
When using AdaBoost to select discriminant features from some feature space (e.g. Gabor feature space) for face recognition, cascade structure is usually adopted to leverage the asymmetry in the distribution of positi... When using AdaBoost to select discriminant features from some feature space (e.g. Gabor feature space) for face recognition, cascade structure is usually adopted to leverage the asymmetry in the distribution of positive and negative samples. Each node in the cascade structure is a classifier trained by AdaBoost with an asymmetric learning goal of high recognition rate but only moderate low false positive rate. One limitation of AdaBoost arises in the context of skewed example distribution and cascade classifiers: AdaBoost minimizes the classification error, which is not guaranteed to achieve the asymmetric node learning goal. In this paper, we propose to use the asymmetric AdaBoost (Asym- Boost) as a mechanism to address the asymmetric node learning goal. Moreover, the two parts of the selecting features and forming ensemble classifiers are decoupled, both of which occur simultaneously in AsymBoost and AdaBoost. Fisher Linear Discriminant Analysis (FLDA) is used on the selected fea- tures to learn a linear discriminant function that maximizes the separability of data among the different classes, which we think can improve the recognition performance. The proposed algorithm is dem- onstrated with face recognition using a Gabor based representation on the FERET database. Ex- perimental results show that the proposed algorithm yields better recognition performance than AdaBoost itself. 展开更多
关键词 AsymBoost ADABOOST Gabor feature Face recognition
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MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
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作者 GeGuangying ChenLili XuJianjian 《Journal of Electronics(China)》 2005年第3期321-328,共8页
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving tar... Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively. 展开更多
关键词 Moving targets detection Pattern recognition Wavelet neural network Targets classification
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Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition 被引量:5
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作者 CHEN WenXue LOU HaiYan +9 位作者 ZHANG HongPing NIE Xiu LAN WenXian YANG YongXia XIANG Yun QI JianPin LEI Hao TANG HuiRu CHEN FenEr DENG Feng 《Science China(Life Sciences)》 SCIE CAS 2011年第7期606-616,共11页
Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-an... Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades. 展开更多
关键词 neuroepithelial tumor grade classification high-resolution magic-angle spinning nuclear magnetic resonance (HRMASNMR) spectroscopy METABONOMICS pattern recognition
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