一种改进的支持向量机及其在图像分割中的应用

支持向量机方法是一种基于统计学理论的机器学习的新方法,它在解决小样本,非线性及高维模式识别中表现出许多特有的优势。目前它主要用于二元分类问题中,而对于其在多类分类应用仍是一个值得研究的问题。在目前存在的各种多类支持向量机分类问题中,一对一方法是一种最符合实际的方法。本文提出了一种改进的支持向量机,并将其应用于图像分割。这种改进的支持向量机它对一对一方法进行了改进,实验表明,支持向量机的方法是一种很有潜力的图像分割技术。     

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC₅₀) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu²⁺, Hg²⁺, Cr⁶⁺, and Cd²⁺) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.     

加权支持向量机分类算法及应用

加权支持向量机分类算法是对类别差异造成的影响进行相应补偿的一种支持向量机算法，目的是提高样本中小类别的分类精度．我们通过用支持向量机和加权支持向量机算法相比较，加权支持向量机可提高样本中小类别的分类精度，这对于某些需要重点关注的小类别精度的分类有重要的现实意义．     

支持向量机的研究与应用

通过对支持向量机的历史发展和理论进行分析,阐述了支持向量机相对于以往机器学习算法的优点。通过分析支持向量机的国内外研究现状,对该理论和算法的应用与发展前景进行了探讨。     

基于支持向量机的控制图异常模式参数估计

针对控制图5种异常模式的6个参数,提出基于一对一算法的多类分类支持向量机的控制图异常模式下的参数估计方法.在模型构造中采用遗传算法优化支持向量机的参数.仿真实验结果表明.该方法结构简单,收敛速度快,具有较高的识别精度,适合于控制图异常模式的参数估计.     

基于谐波小波包和支持向量机的声发射源识别

为了解决直升机动部件疲劳损伤类型识别问题,提出了一种基于谐波小波包特征提取和层次支持向量多分类器的声发射源类型识别方法.声发射信号经过4层谐波小波包分解后,提取各个频段的能量特征用于声发射源类型识别,克服了传统小波包分析能量泄露、频带选取不灵活、不同层频率分辨率不同的缺点.首先,利用已知声发射源类型的试验数据训练层次支...     

创新思维在机械设计教学中的运用

本文探讨了如何从教育理念、教育方式、教学方法、教学组织这几个方面将创新思维运用于机械设计课堂教学中。     

基于贝叶斯证据框架下支持向量机建模的迭代优化控制

In the paper,an iterative method is presented to the optimal control of batch processes.Generally it is very difficult to acquire an accurate mechanistic model for a batch process.Because support vector machine is powerful for the problems characterized by small samples,nonlinearity,high dimension and local minima,support vector regression models are developed for the optimal control of batch processes where end-point properties are required.The model parameters are selected within the Bayesian evidence framework.Based on the model,an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy.Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.     

Classification analysis of microarray data based on ontological engineering

Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.     

Probability output of multi-class support vector machines

A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.     

Fault diagnosis model based on fuzzy support vector machine combined with weighted fuzzy clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.     

Support vector machine for predicting protein interactions using domain scores

Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.     

Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice

Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that N1R spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.     

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

INTRODUCTION Recent techniques based on oligonucleotide or cDNA microarrays allow the expression level of thousands of genes to be monitored in parallel (Golub et al., 1999). A critically important factor for cancer diagnosis and treatment is the reliable prediction of tumor progression. A remarkable advance for mo- lecular biology and for cancer research is cDNA mi- croarray technology. cDNA microarray datasets havea high dimensionality corresponding to the large number of genes monit…     

基于支持向量机的人脸融合特征提取方法

针对人脸识别系统中的主成分分析和线性判别分析两种特征提取方法的优缺点,提出了一个融合特征提取方法,并构造了一个能够将图像数据空间的人脸映射到人脸特征空间中并实施识别的实验系统。最后分析了该系统的构成与特点,并给出了实验测试结果。     

基于支持向量机的siRNA降解效率预测

为了辅助siRNA的设计,从已发表文献中共收集到573个siRNA的实验数据,使用基于统计学习理论的支持向量机(SVM)方法,提取了siRNA序列的碱基对关联性(BBC)特征,然后使用十倍交叉验证方法,对siRNA沉默目标基因的效率进行了预测.结果表明,基于支持向量机,选用多项式核作为核函数的算法具有最高的AUC值(0.73,ROC曲线图)和最高的r值(0.43,Pearson相关系数分析),优于以前基于打分的算法.结果说明,在以后的siRNA的设计中应该更多关注碱基之间的关联信息.     

Nonlinear decoupling controller design based on least squares support vector regression

INTRODUCTION Most practical systems are multivariate nonlin- ear systems. In general, the MIMO (multiple inputs and multiple outputs) systems are coupled. This cou- pling affects the effectiveness of a specific loop con- troller on the corresponding output, and in some case, may become serious and cause many difficulties to the control system design. How to decouple the mul- tivariate systems and design practical controllers is one of the major issues in nonlinear control area. In recen…     

基于支持向量机的酵母重组热点和冷点的预测

使用基于统计学习理论的支持向量机(SVM)方法,提出了针对重组热点和冷点分类预测的新方法.对酵母基因组的303个重组热点开放阅读框(hot ORF)以及48个重组冷点开放阅读框(cold ORF),提取了序列的一般二联碱基丰度特征,以及基于密码子使用偏性的二联碱基丰度特征,然后使用二倍交叉验证方法,选择不同的核函数和对应参数,对数据集进行了训练和分类预测.研究结果表明,当使用径向基核函数,并采用基于密码子使用偏性的二联碱基丰度特征时,预测准确率为87·47%.     

基于自适应约简相关向量机的光照色度估计

提出了一种新的自适应约简相关向量机回归算法来估计图像的光照色度以达到色彩一致性目的.在稀疏贝叶斯学习的框架下,该算法首先以多核形式自适应结合全局核函数和局部核函数扩展相关向量机,然后应用改进的保局投影来约简多核输入矩阵的列维数以减少训练时间.为了估计光照色度,通过图像色度直方图的模糊中心值和其相应光源值训练算法.基于真实图像的实验表明所提算法优于支持向量机和相关向量机且其训练时间小于相关向量机.