期刊文献+

基于支持向量机的独立学院教师计算机评价系统的研究

Study on the Independent College Teacher Computer Evaluation System Based on SVM
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摘要 独立学院是现在高等教育体系的重要组成部分,为提高教学质量,促进教师自身素质的优化和教师教学能力的提高有必要对教师的能力进行评估。在现有评估教师方法的基础上,提出了采用基于支持向量机的方法对教师的能力进行评价。 Independent college is the important constituent of higher education system,and it is necessary to evaluate teacher's ability in order to improve the quality of teaching,promote teachers' quality and enhance the teachers' teaching ability.In this paper,on the basis of the existing teachers' assessment,a method based on support vector machine(SVM) method to evaluate the teachers' ability was proposed.
出处 《农业网络信息》 2010年第10期116-118,共3页 Agriculture Network Information
基金 吉林省优秀课程"计算机组成与体系结构"(编号JSYK-20090710) 校级优秀课"大学计算机基础" 吉林省教育厅"十一五"项目"计算机技术在吉林玉米生产中应用的研究与实践"吉科教合字[2008]第304号 校级课题"依托科研平台 创建独立院校高素质信息化人才培养模式的研究与实践"发展教研-09-03"吉林玉米栽培智能专家系统研究与应用"发展科研-09-16
关键词 支持向量机 核函数 教师评价 归一化 kernel function of support vector machine(SVM) kernel functiont eacher evaluation normalization
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参考文献7

  • 1陈婕.对高校人力资源管理有关问题的探讨[J].江苏海洋大学学报(人文社会科学版),2005,3(1):92-94. 被引量:10
  • 2Rocco C M, Moreno J A.Fast nonte carlo reliability evaluation using support vector machine[J].Reliability Engineering and System Safety, 2002, 76: 237-243.
  • 3Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag,1995,1-175.
  • 4Osuna E,Girosi F. Reducing the run-time complexity of support vector machines.In:Scholkopf B, Burges C,Smola A, et al. Advances in kernel methods-support vector learning. Cambridge, MA:MIT Press, 1999,271-283.
  • 5Vapnik V N.Three fundamental concepts of the capacity of learning machines[J].Physica (A), 1993,200:538-544.
  • 6董春曦,杨绍全,饶鲜,汤建龙.支持向量机推广能力估计方法比较[J].电路与系统学报,2004,9(4):86-91. 被引量:11
  • 7张欣,戴永.多类SVM分类方法在智能像卡识别中的实现[J].计算机工程与应用,2007,43(29):104-106. 被引量:2

二级参考文献28

  • 1唐发明,王仲东,陈绵云.一种新的二叉树多类支持向量机算法[J].计算机工程与应用,2005,41(7):24-26. 被引量:50
  • 2张一驰.人力资源管理教程[M].北京:北京大学出版社,1999..
  • 3Vladimir N Vapnik. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, Inc, 2000.
  • 4Burges J C. A Tutorial on Support Vector Machines for Pattern Recognition [M]. Boston: Kluwer Academic Publishers,. 1999.
  • 5Grace Wahba. An Introduction to Model Building with Reproducing Kernel Hilbert Spaces [R/OL]. TECHNICAL REPORT NO.1020, available at http://www.stat.wisc.edu/?wahba. 2000.
  • 6Lunts A, Brailovskiy V. Evaluation of Attributes Obtained in Statistical Decision Rules [J]. Engineering Cybernetics, 1967, 3: P98-109.
  • 7Vapnik V, Chapelle O. Choosing Multiple Parameters for Support Vector Machine [J]. Machine Learning, 2002, 46(1-3).
  • 8Jaakkola T, Haussler D. Probabilistic Kernel Regression Models [A]. Proceedings of the Seventh Workshop on AI and Statistics [C]. San Francisco, 1999.
  • 9Wahba G, Lin Yi, et al. Generalized Approximate Cross Validation for Support Vector Machines or Another Way to Look at Margin-like Quantities [A]. Advances in Large Margin Classifiers [C]. MIT Press. 2000, 297-209.
  • 10Opper M, Winther O. Gaussian Processes and SVM: Mean Field and Leave-one-out [A]. Advances in Large Margin Classifiers [C]. Cambridge, MA: MIT Press, 2000, 311-326.

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