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Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach 被引量:1
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作者 Artemio Sotomayor-Olmedo Marco A. Aceves-Fernández +3 位作者 Efrén Gorrostieta-Hurtado Carlos Pedraza-Ortega Juan M. Ramos-Arreguín J. Emilio Vargas-Soto 《International Journal of Intelligence Science》 2013年第3期126-135,共10页
The development of forecasting models for pollution particles shows a nonlinear dynamic behavior;hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollut... The development of forecasting models for pollution particles shows a nonlinear dynamic behavior;hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques. 展开更多
关键词 PREDICTIVE Models AIRBORNE POLLUTION support vector Machines kernel Functions
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Incremental support vector machine algorithm based on multi-kernel learning 被引量:7
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作者 Zhiyu Li Junfeng Zhang Shousong Hu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期702-706,共5页
A new incremental support vector machine (SVM) algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set l... A new incremental support vector machine (SVM) algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision. 展开更多
关键词 support vector machine (SVM) incremental learning multiple kernel learning (MKL).
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Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines 被引量:2
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作者 刘涵 刘丁 邓凌峰 《Chinese Physics B》 SCIE EI CAS CSCD 2006年第6期1196-1200,共5页
Support vector machines (SVM) have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel i... Support vector machines (SVM) have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction. 展开更多
关键词 support vector machines chaotic time series prediction fuzzy sigmoid kernel
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A Novel Kernel for Least Squares Support Vector Machine
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作者 冯伟 赵永平 +2 位作者 杜忠华 李德才 王立峰 《Defence Technology(防务技术)》 SCIE EI CAS 2012年第4期240-247,共8页
Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel... Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms. 展开更多
关键词 计算技术 理论 方法 自动机理论
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A Kernel Clustering Algorithm for Fast Training of Support Vector Machines
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作者 刘笑嶂 冯国灿 《Journal of Donghua University(English Edition)》 EI CAS 2011年第1期53-56,共4页
A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickl... A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy. 展开更多
关键词 support vector machines(SVMs) sample reduction topdown hierarchical clustering kernel bisecting k-means
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Support Vector Machine:A Novel Tool for Mineral Prospectivity Mapping 被引量:1
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作者 Renguang Zuo~1,Gang Chen~2 1.State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan 430074,China. 2.Faculty of Information Engineering,China University of Geosciences(Wuhan),Wuhan 430074,China 《地学前缘》 EI CAS CSCD 北大核心 2009年第S1期289-289,共1页
Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with... Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained 展开更多
关键词 support vector MACHINE kernel function prospectivity NEURAL Network TIBET
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Temperature prediction control based on least squares support vector machines 被引量:5
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作者 BinLIU HongyeSU +1 位作者 WeihuaHUANG JianCHU 《控制理论与应用(英文版)》 EI 2004年第4期365-370,共6页
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant i... A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm. 展开更多
关键词 Predictive control Least squares support vector machines RBF kernel function Generalized prediction control
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Mandarin Digits Speech Recognition Using Support Vector Machines 被引量:2
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作者 谢湘 匡镜明 《Journal of Beijing Institute of Technology》 EI CAS 2005年第1期9-12,共4页
A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speec... A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33%, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited. 展开更多
关键词 speech recognition support vector machine (SVM) kernel function
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Application of wavelet support vector regression on SAR data de-noising 被引量:2
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作者 Yi Lin Shaoming Zhang +1 位作者 Jianqing Cai Nico Sneeuw 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期579-586,共8页
A new filtering method for SAR data de-noising using wavelet support vector regression (WSVR) is developed. On the basis of the grey scale distribution character of SAR imagery, the logarithmic SAR image as a noise ... A new filtering method for SAR data de-noising using wavelet support vector regression (WSVR) is developed. On the basis of the grey scale distribution character of SAR imagery, the logarithmic SAR image as a noise polluted signal is taken and the noise model assumption in logarithmic domain with Gaussian noise and impact noise is proposed. Based on the better per- formance of support vector regression (SVR) for complex signal approximation and the wavelet for signal detail expression, the wavelet kernel function is chosen as support vector kernel func- tion. Then the logarithmic SAR image is regressed with WSVR. Furthermore the regression distance is used as a judgment index of the noise type. According to the judgment of noise type every pixel can be adaptively de-noised with different filters. Through an approximation experiment for a one-dimensional complex signal, the feasibility of SAR data regression based on WSVR is con- firmed. Afterward the SAR image is treated as a two-dimensional continuous signal and filtered by an SVR with wavelet kernel function. The results show that the method proposed here reduces the radar speckle noise effectively while maintaining edge features and details well. 展开更多
关键词 synthetic aperture radar (SAR) support vector regres-sion (SVR) kernel function wavelet analysis function approximation.
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Signal Classification Method Based on Support Vector Machine and High-Order Cumulants 被引量:1
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作者 Xin ZHOU Ying WU Bin YANG 《Wireless Sensor Network》 2010年第1期48-52,共5页
In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as c... In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust. 展开更多
关键词 HIGH-ORDER CUMULANTS support vector Machine kernel Function SIGNAL Classification
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Blind source separation algorithm based on support vector machines 被引量:1
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作者 HE Xuan-sen HU Bo-ping 《通讯和计算机(中英文版)》 2008年第11期7-12,共6页
关键词 通信技术 盲源分离算法 计算方法 径向基函数 概率密度函数
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Improved Scheme for Fast Approximation to Least Squares Support Vector Regression
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作者 张宇宸 赵永平 +3 位作者 宋成俊 侯宽新 脱金奎 叶小军 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第4期413-419,共7页
The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FS... The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR. 展开更多
关键词 support vector regression kernel method least squares SPARSENESS
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Heating load interval forecasting approach based on support vector regression and error estimation
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作者 张永明 于德亮 齐维贵 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第4期94-98,共5页
As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this p... As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching. 展开更多
关键词 heating supply energy-saving load forecasting support vector regression nonparametric kernel estimation confidence interval
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The seam offset identification based on support vector regression machines
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作者 曾松盛 石永华 +1 位作者 王国荣 黄国兴 《China Welding》 EI CAS 2009年第2期75-80,共6页
The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly... The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction. 展开更多
关键词 support vector regression machine data-dependent kernel function offset identification mean filtering
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Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model
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作者 Yang Sun Longwei Chen 《Journal of Applied Mathematics and Physics》 2021年第12期3122-3142,共21页
With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly af... With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers. 展开更多
关键词 CNN Twin support vector Machine Wavelet kernel Function Traffic Sign Recognition Transfer Learning
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Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme
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作者 P.Ramya B.Karthik 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期2379-2391,共13页
Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the... Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool. 展开更多
关键词 Text classification word sense disambiguation kernel support vector machine learning algorithm cuckoo search optimization feature extraction
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ERROR ANALYSIS OF MULTICATEGORY SUPPORT VECTOR MACHINE CLASSIFIERS
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作者 Lei Ding BaohuaiSheng 《Analysis in Theory and Applications》 2010年第2期153-173,共21页
The paper is related to the error analysis of Multicategory Support Vector Machine (MSVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error e... The paper is related to the error analysis of Multicategory Support Vector Machine (MSVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error estimate with De La Vall6e Poussin means. We also introduce the standard estimation of sample error, and derive the explicit learning rate. 展开更多
关键词 support vector machine classification learning rate reproducing kernel Hilbert spaces De La Vall^e Poussin means
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多策略改进黏菌算法阶段优化HSVM变压器故障辨识 被引量:2
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作者 谢国民 林忠宝 《电子测量与仪器学报》 CSCD 北大核心 2024年第3期67-76,共10页
为解决变压器故障诊断精度较低的问题,提出了一种多策略改进黏菌算法(ISMA)阶段优化混合核支持向量机(HSVM)的变压器故障诊断新方法。首先,利用主成分分析(PCA)来消除变量之间的信息冗余并降低数据集维度。其次,引入黏菌算法(SMA),并结... 为解决变压器故障诊断精度较低的问题,提出了一种多策略改进黏菌算法(ISMA)阶段优化混合核支持向量机(HSVM)的变压器故障诊断新方法。首先,利用主成分分析(PCA)来消除变量之间的信息冗余并降低数据集维度。其次,引入黏菌算法(SMA),并结合Logistic混沌映射、二次插值、自适应权重多策略改进SMA,以提高SMA算法收敛速度和局部搜索能力;然后,与原始SMA、WHO和GWO算法进行寻优测试,对比验证改进后SMA算法的优越性;最后,使用改进SMA算法分阶段对混合核支持向量机参数寻优,构建ISMA-HSVM变压器故障诊断模型。将降维后的特征数据输入HSVM模型与BPPN、ELM和SVM进行比较,HSVM模型的诊断准确性分别提高了5.55%、8.89%、5.55%。使用ISMA优化HSVM模型参数,与WHO、GWO、SMA算法优化效果比较,结果准确性提高了13.33%、12.22%、5.55%。其中,ISMA-HSVM模型的诊断精度为93.33%。实验结果表明,所提模型有效提升故障诊断分类性能,且具有较高的故障诊断精度。 展开更多
关键词 故障诊断 主成分分析 黏菌算法 混合核支持向量机
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基于CNN-SVM的输送带纵向撕裂检测方法研究
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作者 张释如 余文瑾 王锐 《煤炭技术》 CAS 2024年第7期201-204,共4页
针对传统基于机器学习的输送带纵向检测方法中,特征提取过分依赖于人工设计的缺陷,采用卷积神经网络(CNN)对输送带纵向撕裂特征自动进行提取。但CNN中自带的Softmax分类器不能较好地解决非线性问题,而支持向量机(SVM)能利用核函数将向... 针对传统基于机器学习的输送带纵向检测方法中,特征提取过分依赖于人工设计的缺陷,采用卷积神经网络(CNN)对输送带纵向撕裂特征自动进行提取。但CNN中自带的Softmax分类器不能较好地解决非线性问题,而支持向量机(SVM)能利用核函数将向量映射到高维空间,将线性不可分的问题在高维空间线性可分,有效解决非线性问题,因此采用SVM代替CNN自带的Softmax分类器,提出一种基于CNN-SVM的输送带纵向撕裂检测方法。此外,在CNN中引入批量归一化(BN)、Dropout处理和SE注意力机制进行改进。实验结果表明,基于CNN-SVM的输送带纵向撕裂检测方法准确率可达到99.46%,有效提高了输送带纵向撕裂的检测准确率。 展开更多
关键词 纵向撕裂检测 特征提取 卷积神经网络 支持向量机 核函数
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基于改进SKNet-SVM的网络安全态势评估 被引量:2
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作者 赵冬梅 孙明伟 +1 位作者 宿梦月 吴亚星 《应用科学学报》 CAS CSCD 北大核心 2024年第2期334-349,共16页
为提高网络安全态势评估的准确率,增强稳定性与鲁棒性,提出一种基于改进选择性卷积核卷积神经网络和支持向量机的网络安全态势评估模型。首先,使用改进选择性卷积核代替传统卷积核进行特征提取,提高卷积神经网络感受野变化的自适应性,... 为提高网络安全态势评估的准确率,增强稳定性与鲁棒性,提出一种基于改进选择性卷积核卷积神经网络和支持向量机的网络安全态势评估模型。首先,使用改进选择性卷积核代替传统卷积核进行特征提取,提高卷积神经网络感受野变化的自适应性,增强特征之间关联性。然后,将提取的特征输入到支持向量机中进行分类,并使用网格优化算法对支持向量机中的参数进行全局寻优。最后,根据网络攻击影响指标计算网络安全态势值。实验表明,基于改进选择性卷积核卷积神经网络和支持向量机的态势评估模型与传统的卷积神经网络搭建的态势评估模型相比,准确率更高,并且具有更强的稳定性和鲁棒性。 展开更多
关键词 网络安全态势评估 网络安全态势感知 改进选择性卷积核卷积神经网络 支持向量机 网格优化算法
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