基于剪辑最近邻法的股市趋势的模式识别分类研究

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。

基于K-近邻法的股价曲线的模式识别分类研究

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。

基于最近邻法的股市趋势的模式识别分类研究

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。

谈模式识别与分类技术及金融业的“知识经济

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...

支持向量机-微量元素法用于胃癌的模式识别

目的利用支持向量机-微量元素法建立胃癌的模式识别,以期作为胃癌诊断的辅助手段。方法以血浆作为研究对象,采用电感耦合等离子体发射光谱法测定健康者与胃癌患者血清中Zn、Fe、Cu、Ni、Co、Mo、Mn、Cr和Zn/Cu 9个指标,结合支持向量机分类模式,建立胃癌诊断的模型。结果多项式核函数的平均灵敏度为93.0%、平均特异度为91.4%、平均准确度为92.6%;径向基核函数的平均灵敏度为95.0%、平均特异度为88.6%、平均准确度为93.3%。结论采用的支持向量机-微量元素法为胃癌的预防、诊断和临床治疗提供了一种简便的辅助参考方法。

模式分类方法研究

分析了模糊K-近邻方法FKNN,研究了模糊最小最大神经网络FMMNN分类方法,然后深入分析了支持向量机SVM原理,并在此基础上给出了一种改进的径向基核函数。基于IRIS数据,进行了计算机仿真实验,结果表明,改进的SVM方法分类性能比模糊最小最大神经网络与模糊K-近邻算法的分类性能更好,且运算时间更短,更易于实时实现。

基于流形距离的人工免疫无监督分类与识别算法

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.

塔河油田碳酸盐岩储层FMI识别模式研究

FMI测井具有分辨率高、信息量大、可以直观显示井壁地层的微细变化等特点，能够充分体现碳酸盐岩地层的非均质性。通过对塔河油田奥陶系碳酸盐岩地层的成像测井及常规测井资料分析，结合该地区岩性、储集空间，将该地区FMI成像测井划分为十个模式，以提高储层识别的精度。

基于判别分类和回归计算的传感器节点定位

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。

糖尿病、鼻咽癌的头发分析模拟诊断

以糖尿病病人和鼻咽癌病人为例 ,建立了基于头发微量元素含量 ,可用于辅助筛选或识别病人的一种定量指标———动态诊断指标。模拟诊断显示 ,该定量指标具有较高的辨别一个人是否患有某种疾病 (在此 ,为糖尿病或鼻咽癌 )的准确率 ,其实际应用值得进一步探讨。

一种基于BP神经网络模拟电路故障诊断新方法

模拟电路故障诊断就是一个模式分类识别问题,本文提出了一种新的模拟电路故障特征向量提取方法——分布节点电压和电流的组合信息作为模拟电路的故障特征向量,应用BP神经网络改进算法进行模拟电路故障诊断,实验证明所提出的方法是可行的,也是很有价值和潜力的。

Identification and Quantification of Non-Spherical Particles

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.

中医脉诊客观化与数字化研究

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。

DIMENSIONALITY REDUCTION BASED ON SVM AND LDA,AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE

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.

Early-stage Internet traffic identification based on packet payload size

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.

DISCRIMINATIVE REGULARIZATION:A NEW CLASSIFIER LEARNING METHOD

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.

Noise-assisted MEMD based relevant IMFs identification and EEG classification

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.

基于改进ReliefF算法的特征加权FCM

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。

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

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.

OBLIQUE PROJECTION REALIZATION OF A KERNEL-BASED NONLINEAR DISCRIMINATOR

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.