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1.
为了改善支持向量回归机的性能,本文提出了一种利用多核学习解决回归问题的算法(NS-MKR),算法对基本核函数的组合系数施加了Lp范数的约束(p>1),以得到组合系数的非稀疏解,并采用了两步优化方法,首先求解基于加权组合核的标准支持向量回归问题,用于学习拉格朗日乘子,然后采用简单的计算,求得基本核函数的组合系数,这两个步骤交替进行,直到满足事先定义的收敛准则。在人工数据集和真实数据集上的实验表明,相对于传统的单核和稀疏多核支持向量回归方法,本文提出的算法有更好的泛化性能。 相似文献
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针对SURF算法中快速Hessian矩阵行列式检测出的特征点的不连续现象,从而造成的旋转,模糊和光照变化适应性较差的不足,提出一种旋转SURF检测算子的图像配准新方法;该算法通过将SURF算法的积分图像盒子滤波模板逆时针旋转45度,引入一种可以检测角度旋转的滤波核提升检测算子对不同图像变换的匹配性能,保证新的检测算子与原算法较好的结合,同时利用改进的单纯形算法依据输入图像进行参数优化;仿真结果表明,该方法不仅保留了算法的速度优势,缩短了配准时间,而且在图像模糊变换,光照变换和JPEG压缩变换方面性能有明显的提升,此外对视角变换以及小尺度变换性能也有提高。 相似文献
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An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making 总被引:1,自引:0,他引:1
Jianping LiAuthor Vitae Liwei WeiAuthor Vitae Gang LiAuthor Vitae Weixuan XuAuthor Vitae 《Decision Support Systems》2011,51(2):292-298
Credit risk analysis has long attracted a great deal of attention from both academic researchers and practitioners. However, because of the recent financial crisis, this field continues to draw ever increasingly attention. A multiple kernels multi-criteria programming approach based on evolution strategy (ES-MK-MCP) is proposed for credit decision making in this study. We introduce a linear combination of kernel functions to enhance the interpretability of credit classification models, and propose an alternative to optimize the parameters based on the evolution strategy. For illustration purpose, two UCI credit card data sets are used to verify the effectiveness and feasibility of the proposed model. As the experimental results reveal, the proposed ES-MK-MCP model is an efficient tool for credit risk analysis, especially for decision makers to identify the most relevant features. 相似文献
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Shigeo Abe 《Pattern Analysis & Applications》2007,10(3):203-214
In this paper we discuss sparse least squares support vector machines (sparse LS SVMs) trained in the empirical feature space,
which is spanned by the mapped training data. First, we show that the kernel associated with the empirical feature space gives
the same value with that of the kernel associated with the feature space if one of the arguments of the kernels is mapped
into the empirical feature space by the mapping function associated with the feature space. Using this fact, we show that
training and testing of kernel-based methods can be done in the empirical feature space and that training of LS SVMs in the
empirical feature space results in solving a set of linear equations. We then derive the sparse LS SVMs restricting the linearly
independent training data in the empirical feature space by the Cholesky factorization. Support vectors correspond to the
selected training data and they do not change even if the value of the margin parameter is changed. Thus for linear kernels,
the number of support vectors is the number of input variables at most. By computer experiments we show that we can reduce
the number of support vectors without deteriorating the generalization ability.
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
Shigeo AbeEmail: |
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
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A state space differential reproducing kernel (DRK) method is developed for the three-dimensional (3D) analysis of functionally graded material (FGM) sandwich circular hollow cylinders with combinations of simply-supported and clamped edges and under sinusoidally (or uniformly) distributed loads. The strong formulation of this 3D elasticity problem is derived on the basis of the Reissner mixed variational theorem (RMVT), which consists of the Euler–Lagrange equations of this problem and its associated boundary conditions. The primary field variables are expanded as the single Fourier series in the circumferential coordinate, then interpolated in the axial coordinate using the early proposed DRK interpolation functions, and finally the state space equations of this problem are obtained, which represent a system of ordinary differential equations in the thickness coordinate. The present state space DRK solutions can then be obtained by means of the transfer matrix method. In the illustrative examples, three different edge conditions, the simple-simple (SS), simple-clamped (SC), and clamped–clamped (CC) edges, are considered, and the accuracy and convergence of this method are examined by comparing their solutions with the exact 3D ones available in the literature and the solutions using the ANSYS commercial software. 相似文献
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Fengnong Chen Yan Luo Fang Cheng 《International Journal of Food Science & Technology》2012,47(6):1272-1278
Wheat is one of the most consumed grains in the world. The identification of wheat based on surface characteristics is important for the market. This study is aimed at identifying unsound kernels (Triticum durum Desf), including 710 black germ kernels, 627 broken kernels and 1169 sound kernels from several seed distributors in China. The system is mainly composed of a liner charge‐coupled device for image capture and a software package for extracting various morphological, colour and texture features. The models built by partial least squares discriminate analysis, support vector machine discrimination analysis (SVMDA) and principal component analysis‐artificial neural networks for identifying the unsound kernels have been explored. After comparisons of these three methods, it has been found that SVMDA got the best accuracy: 95.1%, 96.0% and 98.3% (black germ kernels, broken kernels and sound kernels). Obviously, the experimental results have shown that SVMDA is the most feasible and effective choice for the identification. 相似文献
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In the real world all events are connected. There is a hidden network of dependencies that governs behavior of natural processes. Without much argument it can be said that, of all the known data-structures, graphs are naturally suitable to model such information. But to learn to use graph data structure is a tedious job as most operations on graphs are computationally expensive, so exploring fast machine learning techniques for graph data has been an active area of research and a family of algorithms called kernel based approaches has been famous among researchers of the machine learning domain. With the help of support vector machines, kernel based methods work very well for learning with Gaussian processes. In this survey we will explore various kernels that operate on graph representations. Starting from the basics of kernel based learning we will travel through the history of graph kernels from its first appearance to discussion of current state of the art techniques in practice. 相似文献