云南高校图书馆联盟文献共享服务平台- SUPPORT_VECTOR

SUPPORT_VECTOR_MACHINES: 作品数：214被引量：655H指数：12; 导出分析报告; 相关作者：孙健国赵永平魏威周健史秀志更多>>; 相关机构：南京航空航天大学中南大学多伦多大学中国石油大学（北京）更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金国家重点基础研究发展计划国家高技术研究发展计划国家教育部博士点基金更多>>

EasySVM: A visual analysis approach for open-box support vector machines被引量：3: 《Computational Visual Media》2017年第2期161-175,共15页Yuxin Ma Wei Chen Xiaohong Ma Jiayi Xu Xinxin Huang Ross Maciejewski Anthony K.H.Tung; supported in part by the National Basic Research Program of China (973 Program, No. 2015CB352503);the Major Program ofNational Natural Science Foundation of China (No. 61232012);the National Natural Science Foundation of China (No. 61422211); Support vector machines(SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the tr...; 关键词：support vector machines(SVMs) rule extraction visual classification high-dimensional visualization visual analysis

Study on phase retardation characteristic of LCVR using dispersion analysis and SVM被引量：4: 《Instrumentation》2015年第2期11-17,共7页HU Dongmei LIU Quan NIU Guocheng ZHU Yifeng YU Lintao; supported by the National Natural Science Foundation of China ( grant no. 91338116);the National Key Basic Research and Development Program ( 973 Plan) ( grant no. 613225); To calibrate the phase retardance of a Liquid crystal variable retarder(LCVR),its birefringence dispersion characteristic was analyzed,and the Support vector machines(SVM) algorithm was adopted to establish the predic...; 关键词：liquid crystal variable retarder(LCVR) birefringence dispersion support vector machines(SVM) phase retardance calibration

Credit scoring by feature-weighted support vector machines被引量：4: 《Journal of Zhejiang University-Science C(Computers and Electronics)》2013年第3期197-204,共8页Jian SHI Shu-you ZHANG Le-miao QIU; Project supported by the National Basic Research Program (973) of China (No. 2011CB706506);the National Natural Science Foundation of China (No. 50905159);the Natural Science Foundation of Jiangsu Province (No. BK2010261);the Fundamental Research Funds for the Central Universities (No. 2011XZZX005),China; Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications ...; 关键词：Credit scoring model Support vector machine(SVM) Feature weight Random forest

Study of tide prediction method influenced by nonperiodic factors based on support vector machines被引量：3: 《Acta Oceanologica Sinica》2012年第5期160-164,共5页HE Shi-jun ZHOU Wenjun ZHOU Ruyan HUANG Dongmei; The Shanghai Committee of Science and Technology of China under contract No. 10510502800;the Graduate Student Education Innovation Program Foundation of Shanghai Municipal Education Commission of China;the National Key Science Foundation Research "973" Project of the Ministry of Science and Technology of China under contract No. 2012CB316200; Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the...; 关键词：tidal prediction support vector machines celestial motion law harmonic analysis BP neural network nonperiodic factors

An efficient global sensitivity analysis approach for distributed hydrological model被引量：12: 《Journal of Geographical Sciences》2012年第2期209-222,共14页SONG Xiaomeng ZHAN Chesheng XIA Jun KONG Fanzhe; National Key Basic Research Program of China,No.2010CB428403;National Grand Science and Technology Special Project of Water Pollution Control and Improvement,No.2009ZX07210-006; Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bott...; 关键词：response surface methodology sensitivity analysis support vector machines RSMSobol method Huaihe River Basin

Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods被引量：17: 《Transactions of Nonferrous Metals Society of China》2011年第12期2734-2743,共10页周健李夕兵史秀志魏威吴帮标; Project (50934006) supported by the National Natural Science Foundation of China;Project (2010CB732004) supported by the National Basic Research Program of China;Project (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China; The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...; 关键词：underground mine pillar stability Fisher discriminant analysis （FDA） support vector machines （SVMs） PREDICTION

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction被引量：1: 《Water Science and Engineering》2010年第4期361-377,共17页Di LIU Zhong-bo YU Hai-shen LV; supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951101);the Program for Changjiang Scholars and Innovative Research Teams in Universities,the Ministry of Education,China (Grant No. IRT0717); Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter...; 关键词：data assimilation support vector machines ensemble Kalman filter soil moisture

Learning General Gaussian Kernels by Optimizing Kernel Polarization被引量：4: 《Chinese Journal of Electronics》2009年第2期265-269,共5页WANG Tinghua HUANG Houkuan TIAN Shengfeng DENG Dayong; The problem of model selection for Support vector machines （SVM） with general Gaussian kernels is considered. Unlike the conventional standard single scale Gaussian kernels, where all the basis functions have a comm...; 关键词：General Gaussian kernels Kernel polar- ization Support vector machines （SVM） Model selection.

Support vector machine ensemble using rough sets theory被引量：1: 《High Technology Letters》2006年第1期58-62,共5页胡中辉 Cai Yunze He Xing Xu Xiaoming; Supported by the High Technology Research and Development Programme of China （2002AA412010）, and the National Key Basic Research and Development Program of China （2002cb312200） and the National Natural Science Foundation of China （60174038）.; A support vector machine （SVM） ensemble classifier is proposed. Performance of SVM trained in an input space eonsisting of all the information from many sources is not always good. The strategy that the original inp...; 关键词：support vector machines rough sets ENSEMBLE attribute reduction decision fusion

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm被引量：11: 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》2005年第10期961-973,共13页毛勇周晓波皮道映孙优贤 WONG Stephen T.C.; Project supported by the National Basic Research Program (973) of China (No. 2002CB312200) and the Center for Bioinformatics Pro-gram Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School, Harvard University, Boston, USA; In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result...; 关键词：Gene selection Support VECTOR machine (SVM) RECURSIVE feature ELIMINATION (RFE) GENETIC algorithm (GA) Parameter SELECTION

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