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基于支持向量机的移动电话顾客满意度评价系统 被引量:4

Customer Satisfaction Degree Evaluation System of Mobile Phones Based on SVM
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摘要 移动电话顾客满意度的评价对于移动电话生产商和经销商的经营管理有着重要的意义。利用支持向量机的全局收敛性和良好的推广能力,提出了一种新的基于支持向量机的移动电话顾客满意度评价系统。归纳了移动电话顾客满意度评价指标的设计原则,给出了具体的评价指标体系,采用支持向量机的1-v-1分类策略建立了顾客满意度的评价模型。仿真结果表明,基于支持向量机的顾客满意度评价模型能够有效地实现顾客满意度的评估。该模型的建立为移动电话供应商提供了一个有力的顾客满意度评价工具。 The customer satisfaction degree evaluation of mobile phones plays an important role in the management of producers and sellers. The SVM has many good properties including global convergence and good ability of extension. A new evaluation method of customer satisfaction degree (CSD) of mobile phone based on SVM is presented. The rules for designing key indexes are discussed. The evaluation index system is given. The CSD evaluation system based on one - vs - one mode of support vector machine is built. The simulation result shows that the system can give a good evaluation for CSD. It provides a powerful evaluation tool of CSD for mobile phone providers.
出处 《计算机仿真》 CSCD 2005年第10期97-100,共4页 Computer Simulation
基金 国家自然科学基金资助项目(60174024)
关键词 移动电话 顾客满意度 评价指标 支持向量机 模型 Mobile phone Customer satisfaction degree Evaluation index Support vector machine Model
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  • 1C J C Burges. A tutorial on support vector machines for pattern recognition [J]. Data Mining and Knowledge Discovery 2, 1998:955-974.
  • 2Barcia E., Striuli A. Quality as a measurement of customer perception and satisfaction in mobile TLC [C]. Proceeding of International Conference on Communication Technology. Beijing, China :IEEE Electronic Industry Publishing House, 1996,400-403.
  • 3G Mihelis, et al. Customer satisfaction measurement in the private bank sector [J]. European Journal of Operational Research, 2001,130(2): 347-360.
  • 4唐晓芬.顾客满意度测评[M].上海:上海科学技术出版社,2002..
  • 5V. Vapnik. The nature of statistical learning theory [M]. New York: Springer Verlag, 1995.
  • 6V. Vapnik, Statistical Learning Theory [M]. New York, 1998.
  • 7Chih-Wei Hsu, Chih-Jen Lin. A comparison of methods for multi-class support vector machines [J]. IEEE Trans. on Neural Networks, 2002,3(13), 415-425.
  • 8Johnson M.D.,Fornell C. A framework for comparing customer satisfaction across individuals and product categories [J]. Journal of Consumer Research, 1991, (12): 267-286.
  • 9L Bottou, etc. Comparison of classifier methods: a case study in handwritten digit recognition[C], Proceedings of the 12th IAPR International. Conference on Computer Vision & Image Processing, 1994, 2, 77-82.
  • 10S Knerr, L Personnaz , G Dreyfus. Single-layer learning revisited: A stepwise procedure for building and training a neural network[M]. in Neurcomputing: Algorithms, Architectures and Applications, J.Fogelman, Ed. New York: Springer-Verlag, 1999.

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