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。     

晚期糖基化终末产物荧光光谱检测法在糖尿病筛查中的应用研究

主要研究人体皮肤晚期糖基化终末产物(Advanced glycation end products,AGE)荧光光谱的检测方法,并对AGE荧光光谱在糖尿病检测中的应用价值进行评估.利用研制的AGE荧光光谱检测装置,分别对73例受试者前臂内侧皮肤组织中AGE的荧光进行检测.同时,采用酶联免疫吸附法(ELISA)对受试者血...     

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．     

人体鼻咽组织的时间分辨自体荧光光谱研究

实验研究了在397 nm半导体脉冲激光激发下,人体离体鼻咽正常和癌变组织在600 nm荧光发射波长处的时间分辨自体荧光光谱特性。利用双指数衰减方程对时间分辨自体荧光光谱进行拟合后,获得相应的荧光强度随时间的指数衰减方程以及荧光平均寿命。人体鼻咽癌变和正常组织在600 nm处的自体荧光平均寿命分别为(2.94±0.51)ns和(4.29±0.71)ns,两者之间存在显著的差异。应用时间分辨光谱技术的诊断灵敏度和特异性分别为75%和100%。初步表明了时间分辨自体荧光光谱在早期鼻咽癌诊断的应用价值,该方法可望与传统的稳态荧光光谱结合起来,进一步提高早期鼻咽癌荧光诊断的准确率。     

基于支持向量机的~(31)P磁共振波谱肝细胞癌诊断

支持向量机是在统计学习理论基础上发展起来的一种新的机器学习方法,在模式识别领域有着广泛的应用。利用基于支持向量机模型的31P磁共振波谱数据对肝脏进行分类,区别肝细胞癌,肝硬化和正常的肝组织。通过对基于多项式核函数和径向基核函数的支持向量机分类器进行比较,并且得到三种肝脏分类的识别率。实验表明基于31P磁共振波谱数据的支持向量机分类模型能够对活体肝脏进行诊断性的预测。     

近红外光谱技术快速检测原料乳中掺杂物

采用近红外光谱技术结合化学计量学方法,对原料乳中常见的2种掺杂物——大豆分离蛋白与植脂末进行定量分析研究。先通过不同光谱预处理方法结合偏最小二乘法（PLS）建模评价不同预处理方法的效果,结果表明通过平滑处理结合多元散射校正（MSC）进行光谱预处理效果最佳,大豆分离蛋白PLS定量模型相关系数（R2）与交叉验证均方差（RMSECV）分别为0.980 9、0.127 5,植脂末PLS模型分别为0.972 2、0.130 8。随后比较了不同建模方法的效果,结果发现：采用径向基神经网络（RBF）对大豆分离蛋白的建模效果最佳,R2为0.999 4,测试集均方根误差为0.003 1;采用广义回归神经网络（GRNN）方法对植脂末建模效果最佳,R2为0.998 9,测试集均方根误差为0.004 5。因此,合理结合近红外光谱技术与化学计量学方法可快速、准确检测原料乳中大豆分离蛋白和植脂末这2种掺杂物含量。     

两种过滤特征基因选择算法的有效性研究

对基因表达谱进行特征基因选择不仅能改善疾病分类方法的效能,而且为寻找与疾病相关的特征基因提供新的途径．通过比较用调整p值的t检验、非参数评分两种特征基因选择算法后和未进行选择时支持向量机(SVM)分类器的分类性能、支持向量(SV)的吻合度、错分样本ID的吻合度和对样本均匀翻倍后的稳定性．结果发现：特征选择后线性、核函数为二阶多项式和径向基的SVM分类性能明显提高;特征选择前后的SV及错分样本ID的吻合度均较高;SVM的稳定性较好．由此得出结论：这两种特征选择算法具有一定的有效性．     

基于二层特征筛选的HIV-1蛋白酶特异位点预测

在抗艾滋病治疗中,HIV-1蛋白酶抑制剂发挥着重要作用。对于HIV-1蛋白酶裂解作用位点的研究有助于找到新的治疗靶点。为了对HIV-1蛋白酶特异位点进行预测,本研究用氨基酸索引数据库(Amino Acid Index,AAIndex)中的531个氨基酸物理化学性质参数直接表征肽样本的结构,通过二层特征筛选,最终将4248个表征参数降为57个表征参数。分别采取四种核函数进行HIV-1蛋白酶特异位点的支持向量机(SVM)建模,并通过10折交叉验证及外部测试集方法来验证建模的准确性。结果表明选取NormalizePolyKernel核函数进行SVM建模效果优于其他核函数(PolyKernel、PUK、RBFKernel),所建立的模型对于训练集的10组交叉验证预测准确率达到93.947%,对于外部测试集的预测正确率达到93.684%。     

显微超光谱成像仪用于大鼠糖尿病视网膜病变研究

将超光谱成像技术与显微镜技术相结合,研制了推帚式显微超光谱成像仪．使用该设备采集了正常、糖尿病和药物治疗大鼠的视网膜组织切片的显微超光谱图像数据．通过对正常对照组、糖尿病组、药物治疗组共40例样本的显微超光谱数据进行处理,获得了3组样本的单波段图像和三波段伪彩色合成图像,并提取了各组样本外核丛的典型透射率光谱曲线．分析这些曲线发现,糖尿病大鼠视网膜外核丛组织细胞在400～800nm光谱范围内的透射率整体高于正常大鼠,并且在第180波段附近出现一个小的吸收峰,注射LCVS1001药物治疗后大鼠视网膜外核丛组织的透射率介于正常组与糖尿病组之间,且180波段附近的吸收峰变弱,因此该药物对大鼠糖网病有一定的治疗作用．通过对3组样本的图像和光谱特征的分析表明,可以将显微超光谱成像仪作为一种新的手段,辅助医学研究人员对糖网病的发病和致盲机制以及各种药物的治疗效果进行研究．     

实时荧光聚合酶链反应检测性病患者泌尿生殖道沙眼衣原体的临床分析

采用实时荧光聚合酶链反应（PCR ）检测性病患者泌尿生殖道沙眼衣原体,进行临床和实验室分析。采集380例男女性病患者泌尿生殖道分泌物,进行多形核白细胞数检测和荧光PCR。结果显示,216例男性患者中,尿道多形核白细胞数≥5个者占92．59％,荧光PCR检测沙眼衣原体阳性62例,阳性率为28．70％;沙眼衣原体合并淋病奈瑟菌感染30例,合并感染率为13．89％。164例女性患者中,87例宫颈管内多形核白细胞数≥10个（占53．05％）,荧光PCR检测沙眼衣原体阳性33例（占20．12％）。结果提示,性病门诊开展实时荧光PCR检测沙眼衣原体可提高检测阳性率和控制沙眼衣原体传播。     

Real-Time Classification of EMG Myo Armband Data Using Support Vector Machine

ObjectivesThis study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results.Material and methods:EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition.Results14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set.ConclusionThe greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures.     

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data

Background

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.

Methodology/Principal Findings

Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.

Conclusions/Significance

The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.     

A cross-validation study to select a classification procedure for clinical diagnosis based on proteomic mass spectrometry

We present an approach to construct a classification rule based on the mass spectrometry data provided by the organizers of the "Classification Competition on Clinical Mass Spectrometry Proteomic Diagnosis Data." Before constructing a classification rule, we attempted to pre-process the data and to select features of the spectra that were likely due to true biological signals (i.e., peptides/proteins). As a result, we selected a set of 92 features. To construct the classification rule, we considered eight methods for selecting a subset of the features, combined with seven classification methods. The performance of the resulting 56 combinations was evaluated by using a cross-validation procedure with 1000 re-sampled data sets. The best result, as indicated by the lowest overall misclassification rate, was obtained by using the whole set of 92 features as the input for a support-vector machine (SVM) with a linear kernel. This method was therefore used to construct the classification rule. For the training data set, the total error rate for the classification rule, as estimated by using leave-one-out cross-validation, was equal to 0.16, with the sensitivity and specificity equal to 0.87 and 0.82, respectively.     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

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。     

Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.     

Support vector machines for prediction of peptidyl prolyl cis/trans isomerization.

A new method for peptidyl prolyl cis/trans isomerization prediction based on the theory of support vector machines (SVM) was introduced. The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this study, six training datasets consisting of different length local sequence respectively were used. The polynomial kernel functions with different parameter d were chosen. The test for the independent testing dataset and the jackknife test were both carried out. When the local sequence length was 20-residue and the parameter d = 8, the SVM method archived the best performance with the correct rate for the cis and trans forms reaching 70.4 and 69.7% for the independent testing dataset, 76.7 and 76.6% for the jackknife test, respectively. Matthew's correlation coefficients for the jackknife test could reach about 0.5. The results obtained through this study indicated that the SVM method would become a powerful tool for predicting peptidyl prolyl cis/trans isomerization.     

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.