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• 自动化研究所 [4]
• 计算技术研究所 [2]
• 长春光学精密机械与物... [1]
• 新疆理化技术研究所 [1]
• 国家天文台 [1]
• 中国科学院大学 [1]
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• OAI收割 [12]
• iSwitch采集 [1]

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• 期刊论文 [10]
• 会议论文 [2]
• 学位论文 [1]

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• 2020 [1]
• 2018 [1]
• 2017 [3]
• 2016 [1]
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浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 A Comparison of Correlation Filter-Based Trackers and Struck Trackers 期刊论文  OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 卷号: 30, 期号: 9, 页码: 3106-3118 作者:  Wang, Jinqiao;  Zheng, Linyu;  Tang, Ming;  Feng, Jiayi   |  收藏  |  浏览/下载：33/0  |  提交时间：2021/01/07 Correlation  Target tracking  Kernel  Support vector machines  Training  Optimization  Discrete Fourier transforms  Visual tracking  correlation filters  structured output SVM tracker  struck  ranking SVM tracker   Matrix-Regularized Multiple Kernel Learning via (r, p) Norms 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4997-5007 作者:  Han, Yina;  Yang, Yixin;  Li, Xuelong;  Liu, Qingyu;  Ma, Yuanliang   |  收藏  |  浏览/下载：47/0  |  提交时间：2018/10/23 Generalization Bound  Matrix Regularization  Multiple Kernel Learning (Mkl)  Support Vector Machine (Svm)   Highly Efficient Framework for Predicting Interactions Between Proteins 期刊论文  OAI收割 IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 3, 页码: 731-743 作者:  You, Zhu-Hong;  Zhou, MengChu;  Luo, Xin;  Li, Shuai   |  收藏  |  浏览/下载：21/0  |  提交时间：2018/03/15 Big data  feature extraction  kernel extreme learning machine (K-ELM)  low-rank approximation (LRA)  protein-protein interactions (PPIs)  support vector machine (SVM)   Highly Efficient Framework for Predicting Interactions Between Proteins 期刊论文  OAI收割 IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 3, 页码: 731-743 作者:  You, ZH (You, Zhu-Hong);  Zhou, MC (Zhou, MengChu);  Luo, X (Luo, Xin);  Li, S (Li, Shuai) 收藏  |  浏览/下载：33/0  |  提交时间：2017/03/23 Big data  feature extraction  kernel extreme learning machine (K-ELM)  low-rank approximation (LRA)  protein-protein interactions (PPIs)  support vector machine (SVM)   Discriminating Bipolar Disorder from Major Depression Based on Kernel Svm Using Functional Independent Components 会议论文  OAI收割 Tokyo, Japan., 2017/9/25-28 作者:  Shuang Gao;  Elizabeth A Osuch;  Michael Wammes;  Jean Théberge;  Tianzi Jiang   |  收藏  |  浏览/下载：23/0  |  提交时间：2018/03/09 Independent Component Analysis  Linear Subspace  Kernel Svm  Bipolar Disorder  Major Depression Disorder  Fmri Data  Schizophrenia  Unipolar  Amygdala   Support vector analysis of large-scale data based on kernels with iteratively increasing order 期刊论文  OAI收割 JOURNAL OF SUPERCOMPUTING, 2016, 卷号: 72, 期号: 9, 页码: 3297-3311 作者:  Chen, Bo-Wei;  He, Xinyu;  Ji, Wen;  Rho, Seungmin;  Kung, Sun-Yuan   |  收藏  |  浏览/下载：21/0  |  提交时间：2019/12/13 Support vector analysis  Big data analysis  Kernel ridge regression (KRR)  Ridge support vector machine (Ridge SVM)   Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets 期刊论文  OAI收割 IEEE TRANSACTIONS ON CYBERNETICS, 2014, 卷号: 44, 期号: 1, 页码: 1-20 Wang, Shitong; Wang, Jun; Chung, Fu-lai   |  收藏  |  浏览/下载：22/0  |  提交时间：2014/12/16 Kernel density estimate (KDE)  kernel methods  quadratic programming (QP)  sampling  support vector machine (SVM)   Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Gao X.;  Yu P.;  Yu P. 收藏  |  浏览/下载：20/0  |  提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.   Kernel subclass convex hull sample selection method for svm on face recognition 期刊论文  iSwitch采集 Neurocomputing, 2010, 卷号: 73, 期号: 10-12, 页码: 2234-2246 作者:  Zhou, Xiaofei;  Jiang, Wenhan;  Tian, Yingjie;  Shi, Yong 收藏  |  浏览/下载：44/0  |  提交时间：2019/05/10 Svm  Classification  Sample selection  Kernel  Face recognition   Building Sparse Multiple-Kernel SVM Classifiers 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 卷号: 20, 期号: 5, 页码: 827-839 作者:  Hu, Mingqing;  Chen, Yidiang;  Kwok, James Tin-Yau   |  收藏  |  浏览/下载：19/0  |  提交时间：2019/12/16 Gradient projection  kernel methods  multiple-kernel learning (MKL)  sparsity  support vector machine (SVM)