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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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Support Vector Clustering Uncovered:Insights,Challenges,and Future Outlook 认领 引用
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作者 M.Tanveer Mohammad Tabish +2 位作者 Anuradha Kumari Ashwani Kumar Malik Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期749-775,共27页
Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique... Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique ability to handle noise,outliers,and clusters of diverse,irregular shapes sets it apart from traditional clustering methods.SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data.SVC maps data to a higher-dimensional space,encloses it in a minimal sphere,and identifies clusters when mapped back,supporting complex shapes and ensuring optimality through kernel functions.This review paper provides a comprehensive analysis of the SVC algorithms,exploring their variants such as robust,sparse,and fuzzy-based models and adaptations for large-scale data.Moreover,we analyze the potential of twin support vector clustering(TWSVC),with an emphasis on the use of various loss functions.Finally,the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC.These include advancements in feature engineering,extension to semi-supervised and weakly supervised learning,and the integration of multi-view and multi-modal data.Our work aims to deepen the understanding of SVC,fostering advancements that address the evolving needs of clustering in real-world scenarios. 展开更多
关键词 Clustering kernel methods support vector machine(SVM) twin support vector clustering (TWSVC) unsupervised learning
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Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel 认领 引用 被引量：25
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作者 包哲静 皮道映 孙优贤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第5期691-697,共7页
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a... Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm. 展开更多
关键词 nonlinear model predictive control support vector machine with multi-kernel nonlinear system identification kernel function
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Adjustable entropy function method for support vector machine 认领 引用 被引量：4
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作者 Wu Qing Liu Sanyang Zhang Leyou 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第5期1029-1034,共6页
Based on KKT complementary condition in optimization theory,an unconstrained non-differential optimization model for support vector machine is proposed.An adjustable entropy function method is given to deal with the p... Based on KKT complementary condition in optimization theory,an unconstrained non-differential optimization model for support vector machine is proposed.An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution.The proposed method can find an optimal solution with a relatively small parameter p,which avoids the numerical overflow in the traditional entropy function methods.It is a new approach to solve support vector machine.The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm. 展开更多
关键词 optimization support vector machine adjustable entropy function Newton algorithm.
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Fuzzy smooth support vector machine with different smooth functions 认领 引用 被引量：5
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作者 Chuandong Qin Sanyang Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期460-466,共7页
Smooth support vector machine （SSVM） changs the normal support vector machine （SVM） into the unconstrained op- timization by using the smooth sigmoid function. The method can be solved under the Broyden-Fletcher-G... Smooth support vector machine （SSVM） changs the normal support vector machine （SVM） into the unconstrained op- timization by using the smooth sigmoid function. The method can be solved under the Broyden-Fletcher-Goldfarb-Shanno （BFGS） algorithm and the Newdon-Armijio （NA） algorithm easily, however the accuracy of sigmoid function is not as good as that of polyno- mial smooth function. Furthermore, the method cannot reduce the influence of outliers or noise in dataset. A fuzzy smooth support vector machine （FSSVM） with fuzzy membership and polynomial smooth functions is introduced into the SVM. The fuzzy member- ship considers the contribution rate of each sample to the optimal separating hyperplane and makes the optimization problem more accurate at the inflection point. Those changes play a positive role on trials. The results of the experiments show that those FSSVMs can obtain a better accuracy and consume the shorter time than SSVM and lagrange support vector machine （LSVM）. 展开更多
关键词 smooth support vector machine （SSVM） fuzzy sig- moid function polynomial smooth function fuzzy membership Broyden-Fletcher-Gddfarb-Shanno （BFGS）.
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A Kernel Clustering Algorithm for Fast Training of Support Vector Machines 认领 引用 被引量：1
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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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POSITIVE DEFINITE KERNEL IN SUPPORT VECTOR MACHINE(SVM) 认领 引用 被引量：3
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作者 谢志鹏 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2009年第2期114-121,共8页
The relationship among Mercer kernel, reproducing kernel and positive definite kernel in support vector machine （SVM） is proved and their roles in SVM are discussed. The quadratic form of the kernel matrix is used t... The relationship among Mercer kernel, reproducing kernel and positive definite kernel in support vector machine （SVM） is proved and their roles in SVM are discussed. The quadratic form of the kernel matrix is used to confirm the positive definiteness and their construction. Based on the Bochner theorem, some translation invariant kernels are checked in their Fourier domain. Some rotation invariant radial kernels are inspected according to the Schoenberg theorem. Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed. 展开更多
关键词 support vector machines（SVMs） mercer kernel reproducing kernel positive definite kernel scaling and wavelet kernel
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Support vector machine with mixture of kernels for automatic image annotation 认领 引用
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作者 田东平 Zhao Xiaofei Shi Zhongzhi 《High Technology Letters》 EI CAS 2013年第3期295-300,共6页
Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for au... Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation. 展开更多
关键词 automatic image annotation （ AIA） support vector machine （ SVM） kernel func-tion principal component analysis （PCA）
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Incremental support vector machine algorithm based on multi-kernel learning 认领 引用 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 认领 引用 被引量：3
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作者 刘涵 刘丁 邓凌峰 《Chinese Physics B》 CAS 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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Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents 认领 引用 被引量：2
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作者 Zhi-Hui Yu Ren-Qiang Yu +6 位作者 Xing-Yu Wang Wen-Yu Ren Xiao-Qin Zhang Wei Wu Xiao Li Lin-Qi Dai Ya-Lan Lv 《World Journal of Psychiatry》 SCIE 2024年第11期1696-1707,共12页
BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers base... BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls. 展开更多
关键词 Major depressive disorder Adolescent Support vector machine Machine learning Resting-state functional magnetic resonance imaging Neuroimaging Biomarker
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Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models 认领 引用 被引量：3
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作者 Stephen Ojo Arif Sari Taiwo P. Ojo 《Open Journal of Applied Sciences》 2022年第6期990-1010,共21页
Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introdu... Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond. 展开更多
关键词 Support Vector Regression Radial Basis Function Machine Learning Path Loss Empirical Deterministic
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Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach 认领 引用 被引量：2
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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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WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION 认领 引用 被引量：1
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作者 Tong Yubing Yang Dongkai Zhang Qishan 《Journal of Electronics(China)》 2006年第4期539-542,共4页
Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support... Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines. 展开更多
关键词 Wavelet kernel function Support Vector Machines （SVM） Sparse approximation Quadratic Programming （QP）
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Application of Least Squares Support Vector Machine for Regression to Reliability Analysis 认领 引用 被引量：45
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作者 郭秩维 白广忱 《Chinese Journal of Aeronautics》 SCIE EI CAS 2009年第2期160-166,共7页
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona... In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for... 展开更多
关键词 mechanism design of spacecraft support vector machine for regression least squares support vector machine for regression Monte Carlo method reliability implicit performance function
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Machine Learning Models for Predicting Vestibular Function After Cochlear Implantation 认领 引用
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作者 Mengya Shen Xiaozhang Zhu +6 位作者 Weirui Zhang Shujin Xue Xingmei Wei Ying Kong Jiaqiang Sun Yongxin Li Haihui Wang 《Journal of Otology》 CSCD 2025年第4期225-235,共11页
Objective:To assess the effectiveness of machine learning in automating the prediction of vestibular abnormalities after cochlear implantation(CI)in patients with sensorineural hearing loss(SNHL),with the goal of deve... Objective:To assess the effectiveness of machine learning in automating the prediction of vestibular abnormalities after cochlear implantation(CI)in patients with sensorineural hearing loss(SNHL),with the goal of developing a practical model that can accurately predict long-term vestibular function outcomes and identify associated risk factors.Methods:Clinical data,including imaging,vestibular evoked myogenic potentials(VEMPs),and auditory information,were collected from patients with sensorineural hearing loss(SNHL)before and after CI.The decision tree algorithm was employed to address missing values and screen pre-CI clinical features.Six machine learning methods were subsequently utilized to predict the relationships between the extracted features and post-CI vestibular dysfunction.The best-performing method determined the ranking of feature importance,which was regarded as risk factors for predicting symptoms and VEMPs results after CI.Results:Logistic regression models effectively predicted both post-CI vestibular dysfunction and abnormal cervical VEMP(c VEMP),with accuracies of 80%and 78%,respectively.The relative importance of the features,in descending order,was as follows:c VEMP latency,c VEMP amplitude,and residual hearing threshold.Moreover,the support vector machine(SVM)model attained an accuracy of 88%in predicting abnormal ocular VEMP(o VEMP)post-CI.For the SVM model,the feature importance ranking was as follows:o VEMP latency,o VEMP amplitude,and residual hearing threshold.Conclusions:This study successfully leverages machine learning techniques,specifically support vector machines(SVM)and logistic regression models,to predict the impact of CI on vestibular function.These predictive models provide valuable insights for presurgical planning and decision-making in CI procedures.Moreover,the findings highlight the critical risk factors associated with vestibular dysfunction,offering a robust reference for guiding vestibular rehabilitation strategies. 展开更多
关键词 cochlear implantation vestibular function vestibular evoked myogenic potential machine learning support vector machines
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Support vector machine based nonlinear model multi-step-ahead optimizing predictive control 认领 引用 被引量：10
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作者 钟伟民 皮道映 孙优贤 《Journal of Central South University of Technology》 2005年第5期591-595,共5页
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established... A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection. 展开更多
关键词 nonlinear model predictive control support vector machine nonlinear system identification kernel function nonlinear optimization
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SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS 认领 引用 被引量：3
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作者 李洪双 吕震宙 岳珠峰 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2006年第10期1295-1303,共9页
Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM... Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations. 展开更多
关键词 structural reliability implicit performance function support vector machine
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Mandarin Digits Speech Recognition Using Support Vector Machines 认领 引用 被引量：3
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作者 XIE Xiang KUANG Jing-ming 《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 speech fea... 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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New predictive control algorithms based on Least Squares Support Vector Machines 认领 引用 被引量：4
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作者 刘斌 苏宏业 褚健 《Journal of Zhejiang University-SCIENCE A》 CAS 2005年第5期440-446,共7页
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlin... Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms. 展开更多
关键词 Least Squares Support Vector Machines Linear kernel function RBF kernel function Generalized predictive control
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