基于优化核参数支持向量机的意识任务分类

根据支持向量机的基本原理,给出一种推广误差上界估计判据,并利用该判据进行最优核参数的自动选取。对三种不同意识任务的脑电信号进行多变量自回归模型参数估计,作为意识任务的特征向量,利用支持向量机进行训练和分类测试。分类结果表明,优化核参数的支持向量机分类器取得了最佳的分类效果,分类正确率明显高于径向基函数神经网络。     

基于遗传算法特征选择的~(31)P磁共振波谱图数据分类

基于31P磁共振波谱图(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)对肝脏数据进行诊断,共分为三种类型:肝癌,肝硬化和正常肝。本文在线性分类器分类前先用遗传算法进行特征选择,选择出最优特征子集。实验中,用线性分类器分别对经过遗传算法特征选择后的最优特征子集分类和对提取的全波谱数据进行分类。实验结果证明,前者方法不仅明显提高了分类的准确率,而且减少了分类器运行的时间,其中31P-MR波谱对活体肝细胞癌的诊断正确率从62.50%提高到89.35%。     

基于多分类支持向量机的基因表达系列分析

基因表达系列分析(Serial analysis of gene expression,SAGE)是一种基因表达数据,反映了细胞内的动态变化。模式识别和可视化方法是分析SAGE数据的基本工具,但是由于缺乏描述数据的统计特性,传统的聚类分析技术不适用于SAGE数据的分析。本文提出了一种基于多分类和支持向量机的SAGE数据的分析法。经过对模拟数据和人类癌症SAGE数据的分析,基于径向基核函数的多分类支持向量机算法"一对一"(one-against-one,OAO)算法提供了比PoissonC和PoissonS更好的分类结果。     

高维蛋白质波谱癌症数据特征提取

高维蛋白质波谱癌症数据分析,一直面临着高维数据的困扰。针对高维蛋白质波谱癌症数据在降维过程中的问题,提出基于小波分析技术和主成分分析技术的高维蛋白质波谱癌症数据特征提取的方法,并在特征提取之后,使用支持向量机进行分类。对8-7-02数据集进行2层小波分解时,分别使用db1、db3、db4、db6、db8、db10、haar小波基,并使用支持向量机进行分类,正确率分别达到98.18%、98.35%、98.04%、98.36%、97.89%、97.96%、98.20%。在进一步提高分类识别正确率的同时,提高了时间率。     

神经网络提高肝细胞癌磁共振波谱诊断正确率

通过评价31磷磁共振波谱(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)来辨别三种诊断类型:肝细胞癌,正常肝和肝硬化。运用反向传输神经网络(BP)和径向基函数神经网络(RBF)分析31P-MRS数据,分别建立神经网络模型,进行肝细胞癌的诊断分类以期提高识别率。实验结果证明,应用神经网络模型后,31P-MR波谱对活体肝细胞癌的诊断正确率从89.47%提高到97.3%,且BP更优于RBF。     

基因表达数据小波降噪与肿瘤识别

建立了基于小波降噪和支持向量机的结肠癌基因表达数据肿瘤识别模型.对试验数据进行小波分解,并利用交叉验证的方法计算试验样本的平均分类准确率,确定小波函数与小波分解层数;引入能量阈值方法对小波分解系数进行阈值处理,达到降噪的目的;提出了基因分类贡献率与主成分分析结合的方法,提取结肠癌样本数据特征;利用支持向量机强大的非线性映射能力,实现对结肠癌样本数据的非线性分类.为了减弱样本集的划分对分类准确率的影响,本文采取Jackknife检验方法对支持向量分类器的分类器检验,其分类准确率为96.77%.试验结果证明了该方法的有效性,该方法对结肠癌的识别具有一定的参考价值.     

基于支持向量机的皮肤自体荧光光谱分类方法在糖尿病筛查中的应用

将63例II型糖尿病患者以及140例正常人皮肤的自体荧光光谱分为训练集和测试集两类,针对常用的四种核函数,运用交叉验证、网格寻优法计算最优分类参数,然后结合训练集建模并对测试集分类,结果显示使用径向基核函数时分类效果相对最佳。在此基础上,构建了一种基于线性核函数与径向基核函数的混合核函数,该核函数对人体皮肤自体荧光光谱的分类效果较之于径向基核函数更优,其分类正确率为82．61％,敏感性为69．57％,特异性为95．65％。研究结果表明支持向量机可用于人体皮肤自体荧光光谱的分类,有助于提高糖尿病筛查的正确率。     

基于不同分类模型的基因芯片癌症诊断方法研究

基因芯片技术的发展为生物信息学带来了机遇,使在基因表达水平上进行癌症诊断成为可能。但基因芯片数据高维小样本的特征也使传统机器学习方法面临挑战。本文利用真实的基因表达数据,测试了目前主要的分类方法和降维方法在癌症诊断方面的效果,通过实验对比发现：基于线性核函数的支持向量机可以有效地分类肿瘤与非肿瘤的基因表达,从而为癌症诊断提供借鉴。     

基于支持向量机的水分生产函数研究

本文针对现有的作物水分生产函数模型拟合精度低,提出基于支持向量回归机的方法拟合作物水分生产函数,并与现有的模型进行比较,拟合结果显示,基于支持向量机的模型拟合明显优于现有模型.     

支持向量机与神经网络的关系研究

支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点,该方法已经广泛用于解决分类和回归问题．本文将结构风险函数应用于径向基函数网络学习中,同时讨论了支持向量回归模型和径向基函数网络之间的关系．仿真实例表明所给算法提高了径向基函数网络的泛化性能．     

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.     

基于统计差表与加权投票的高精度剪接位点预测

基于机器学习的高精度剪接位点识别是真核生物基因组注释的关键.本文采用卡方测验确定序列窗口长度,构建卡方统计差表提取位置特征,并结合碱基二联体频次表征序列;针对剪接位点正负样本高度不均衡这一情形,构建10个正负样本均衡的支持向量机分类器,进行加权投票决策,有效解决了不平衡模式分类问题. HS~3D数据集上的独立测试结果显示,供体、受体位点预测准确率分别达到93.39%、90.46%,明显高于参比方法.基于卡方统计差表的位置特征能有效表征DNA序列,在分子序列信号位点识别中具有应用前景.     

基于SVM 的药物靶点预测方法及其应用

目的:基于已知药物靶点和潜在药物靶点蛋白的一级结构相似性,结合SVM技术研究新的有效的药物靶点预测方法。方法:构造训练样本集,提取蛋白质序列的一级结构特征,进行数据预处理,选择最优核函数,优化参数并进行特征选择,训练最优预测模型,检验模型的预测效果。以G蛋白偶联受体家族的蛋白质为预测集,应用建立的最优分类模型对其进行潜在药物靶点挖掘。结果:基于SVM所建立的最优分类模型预测的平均准确率为81.03%。应用最优分类器对构造的G蛋白预测集进行预测,结果发现预测排位在前20的蛋白质中有多个与疾病相关。特别的,其中有两个G蛋白在治疗靶点数据库(TTD)中显示已作为临床试验的药物靶点。结论:基于SVM和蛋白质序列特征的药物靶点预测方法是有效的,应用该方法预测出的潜在药物靶点能够为发现新的药靶提供参考。     

Pattern recognition of number gestures based on a wireless surface EMG system

Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).     

A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach

We have introduced a new method of protein secondary structure prediction which is based on the theory of support vector machine (SVM). SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range of pattern recognition problems, including object recognition, speaker identification, gene function prediction with microarray expression profile, etc. In these cases, the performance of SVM either matches or is significantly better than that of traditional machine learning approaches, including neural networks.The first use of the SVM approach to predict protein secondary structure is described here. Unlike the previous studies, we first constructed several binary classifiers, then assembled a tertiary classifier for three secondary structure states (helix, sheet and coil) based on these binary classifiers. The SVM method achieved a good performance of segment overlap accuracy SOV=76.2 % through sevenfold cross validation on a database of 513 non-homologous protein chains with multiple sequence alignments, which out-performs existing methods. Meanwhile three-state overall per-residue accuracy Q(3) achieved 73.5 %, which is at least comparable to existing single prediction methods. Furthermore a useful "reliability index" for the predictions was developed. In addition, SVM has many attractive features, including effective avoidance of overfitting, the ability to handle large feature spaces, information condensing of the given data set, etc. The SVM method is conveniently applied to many other pattern classification tasks in biology.     

基于多光谱卫星影像与机器学习算法的松材线虫病受害林分识别研究

松材线虫病(Pine Wilt Disease, PWD)被称为“松树癌症”,具有高传染率和高死亡率,对我国森林资源构成了严重的威胁,对我国的经济、社会和生态造成了重大损失。及时发现并清理疫木是遏制松材线虫病蔓延的有效手段,精准监测疫木是防控松材线虫病的前提,但是现阶段缺少大面积识别松材线虫病疫木的技术方法。本文旨在探索哨兵-2号与Landsat-8遥感卫星影像对受害松林的识别能力,采用随机森林(Random Forest, RF)、支持向量机(Support Vector Machine, SVM)、决策树(Decision Tree, DT)和极端梯度提升(Extreme Gradient Boosting, XGBoost)等4种机器学习算法建立了松材线虫病监测模型。结果表明：基于哨兵-2号影像数据建立的监测模型对受害松林的识别准确率高于Landsat-8遥感卫星影像,其中基于10 m分辨率的影像数据建立的监测模型识别准确率最高,随机森林、决策树、支持向量机和极端梯度提升等算法建立模型的准确率分别达到了79.3%、76.2%、78.7%和78.9%。在3种不同的影像数据集中,RF...     

A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data

This paper investigated application of a machine learning approach (Support vector machine, SVM) for the automatic recognition of gait changes due to ageing using three types of gait measures: basic temporal/spatial, kinetic and kinematic. The gaits of 12 young and 12 elderly participants were recorded and analysed using a synchronized PEAK motion analysis system and a force platform during normal walking. Altogether, 24 gait features describing the three types of gait characteristics were extracted for developing gait recognition models and later testing of generalization performance. Test results indicated an overall accuracy of 91.7% by the SVM in its capacity to distinguish the two gait patterns. The classification ability of the SVM was found to be unaffected across six kernel functions (linear, polynomial, radial basis, exponential radial basis, multi-layer perceptron and spline). Gait recognition rate improved when features were selected from different gait data type. A feature selection algorithm demonstrated that as little as three gait features, one selected from each data type, could effectively distinguish the age groups with 100% accuracy. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.     

基于支持向量机识别真核生物DNA中的翻译起始位点

翻译起始位点(TIS)的识别是真核生物基因预测的关键步骤之一,近年来一直得到研究人员的高度重视。基于TIS附近序列的统计特性,出现了一些辨识TIS的判别方法,但识别精度还有待进一步提高。针对传统支持向量机(SVM)方法中存在的不足,提出了基于数据优化法的SVM,它通过其它统计学模型优化训练数据集,进而提高分类器的辨识精度。实验结果表明基于数据优化法的SVM分类器在翻译起始位点的辨识上可获得比其他判别方法更好的效果。     

基于支持向量机(SVM)的剪接位点识别

剪接位点的识别作为基因识别中的一个重要环节, 一直受到研究人员的关注。考虑到剪接位点附近存在的序列保守性,已有一些基于统计特性的方法被用于剪接位点的识别中,但效果仍有待进一步改进。支持向量机（Ｓｕｐｐｏｒｔ Ｖｅｃｔｏｒ Ｍａｃｈｉｎｅｓ） 作为一种新的基于统计学习理论的学习机,近几年有了很大的发展,已被应用在模式识别的许多问题中。文中将其用于剪接位点的识别中,并针对满足ＧＴ－ ＡＧ 规则的序列样本中虚假剪接位点的样本数远大于真实位点这一特性, 提出了一种基于ＳＶＭ 的平衡取小法以获得更好的识别效果。实验结果表明,应用支持向量机进行剪接位点的识别能更好地提取位点附近保守序列的统计特征,对测试集具有更好的推广能力,并且使用上更加简单。这一结果为剪接位点的识别提供了一种新的方法,同时也为生物大分子研究中结构和位点的识别问题的解决提供了新的线索。