An efficient star acquisition method based on SVM with mixtures of kernels

Zheng, Shibao; Liu, Jian; Tian, Jinwen

doi:10.1016/j.patrec.2004.09.003

Cited by 25 publications

(13 citation statements)

References 23 publications

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“…SVM clustering is a a widely known multivariate analysis technique that inserts observations (samples) into classes (clusters) so that observations in the same cluster are as similar as possible, and items in different clusters are as dissimilar as possible [7] . The mixtures of kernels provide more optimal performance than any single kernel [8] . In the second step of the MQCM model, the mixture kernel-SVM based process programs grouping method is presented to form the statistical quality control batches for the next step of the model.…”

Section: Dynamic Multi-variate Process Quality Control Modelmentioning

confidence: 99%

“…The customers' requirements and preferences are identified and analyzed using a linguistic variable in fuzzy numbers. This linguistic set is shown as F={VL(very low), L(low), M(Medium), H(high), and VH(very high)}, and translated into the fuzzy number as (2,3,4), (4,5,6), (6,7,8), (8,9,10)}. The fuzzy number of the customer special requirement can be expressed as data…”

Section: Model Expressions and Algorithmsmentioning

confidence: 99%

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Customer preferences Fuzzy Clustering based dynamic multivariate process quality control method

Yang

Wang

Zhou

et al. 2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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Abstract. Faced with fierce competition and various customer demands in marketplaces, manufacturers need to determine the appropriate settings of statistical quality control criterion for dynamic batches composed of similar products with different customer preferences so that the best customer satisfaction and minimized production cost can be obtained. To achieve this, functional models of dynamic batch generating and customer preferences related to quality control criterion need to be developed. In this paper, a dynamic statistical quality control model considering customer preferences is suggested to solve such statistical quality control problems. Production batches with appropriate quality control criterion are formed regarding various customer preferences, and the small sample capacity for applying SPC is considered. Our application results in special steel product process quality designs which involves various customer preferences is presented to demonstrate the effectiveness of the proposed method.Introduction.

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Section: Dynamic Multi-variate Process Quality Control Modelmentioning

confidence: 99%

Section: Model Expressions and Algorithmsmentioning

confidence: 99%

Customer preferences Fuzzy Clustering based dynamic multivariate process quality control method

Yang

Wang

Zhou

et al. 2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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show abstract

“…The kernel is a typical example of a global kernel. To gain the advantages of both local kernel and global kernel, we obviously prefer the composite kernels (Zhang et al2006;Wu et al 2007;Zheng et al 2006), which can bring better mapping performance through the proper combination of kernels. In recent years, the research on composite kernels is widely used in many fields (Jiang et al 2007;Zheng et al 2005Zheng et al , 2006.…”

Section: Introductionmentioning

confidence: 99%

“…This way, the number of classification errors is truly minimized, while the maximal margin solution is obtained in the separable case and the proposed method is superior to the classical approach in the sense that it truly solves the ERM problem. Zheng (2006) designed an improved unbiased SVC (USVC). It targets at looking for a better classification result by eliminating the bias item of the decision function.…”

Section: Introductionmentioning

confidence: 99%

Online chaotic time series prediction using unbiased composite kernel machine via Cholesky factorization

et al. 2009

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The kernel method has proved to be an effective machine learning tool in many fields. Support vector machines with various kernel functions may have different performances, as the kernels belong to two different types, the local kernels and the global kernels. So the composite kernel, which can bring more stable results and good precision in classification and regression, is an inevitable choice. To reduce the computational complexity of the kernel machine's online modeling, an unbiased least squares support vector regression model with composite kernel is proposed. The bias item of LSSVR is eliminated by improving the form of structure risk in this model, and then the calculating method of the regression coefficients is greatly simplified. Simultaneously, through introducing the composite kernel to the LSSVM, the model can easily adapt to the irregular variation of the chaotic time series. Considering the real-time performance, an online learning algorithm based on Cholesky factorization is designed according to the characteristic of extended kernel function matrix. Experimental results indicate that the unbiased composite kernel LSSVR is effective and suitable for online time series with both the steep variations and the smooth variations, as it can well track the dynamic character of the series with good prediction precisions, better generalization and stability. The algorithm can also save much computation time comparing to those methods using matrix inversion, although there is a little more loss in time than that with the usage of single kernels.

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“…Especially, the development of the least square SVM (LS-SVM) [7], which resulted in a set of linear equations instead of a quadratic programming problem, extended the application of SVM to on-line applications. The introduction of mapping technique extends further the application of SVM to image processing areas [8], including edge detection [9], interpolation [10], object detection [11], etc. One important point in the SVM is that the data with larger support values can most possibly become the support vectors in the sparse SVM, since the sparse process exploits the fact that the support values have a physical meaning in the sense that they reveal the relative importance of the data points for contributing to the SVM model [12].…”

Section: Introductionmentioning

confidence: 99%

X-ray image denoising with directional support value transform

et al. 2009

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Under the support vector machine framework, the support value analysis-based image fusion has been studied, where the salient features of the original images are represented by their support values. The support value transform (SVT)-based image fusion approach have demonstrated some advantages over the existing methods in multisource image fusion. In this paper, the directional support value transform (DSVT) is applied to the denoising of some standard images embedded in white noise and the X-ray images. This directional transform is not norm-preserving and, therefore, the variance of the noisy support values will depend on the scales. And then we use the hard-thresholding rule for estimating the unknown support values in different scales and the thresholding is scale-dependent. The peak signal noise ratio (PSNR) is used as an "objective" measure of performance, and our own visual capabilities are used to identify artifacts whose effects may not be well-quantified by the PSNR value. The experimental results demonstrate that simple thresholding of the support values in the proposed method is very competitive with techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms.

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An efficient star acquisition method based on SVM with mixtures of kernels

Cited by 25 publications

References 23 publications

Customer preferences Fuzzy Clustering based dynamic multivariate process quality control method

Customer preferences Fuzzy Clustering based dynamic multivariate process quality control method

Online chaotic time series prediction using unbiased composite kernel machine via Cholesky factorization

X-ray image denoising with directional support value transform

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