Application research of robust LS-SVM regression model in forecasting patent application counts

Li Wei Zhang; Qian Zhang; Xue Feng Wang; Dong Hua Zhu

Application research of robust LS-SVM regression model in forecasting patent application counts

Li Wei Zhang^*
, Qian Zhang
, Xue Feng Wang
, Dong Hua Zhu

^*此作品的通讯作者

Beijing Institute of Technology
Capital University of Economics and Business
Gachon University

科研成果: 期刊稿件 › 文章 › 同行评审

4 引用（Scopus）

摘要

A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

源语言	英语
页（从-至）	497-501
页数	5
期刊	Journal of Beijing Institute of Technology (English Edition)
卷	18
期	4
出版状态	已出版 - 12月 2009

其它文件与链接

链接到 Scopus 的出版物

引用此

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title = "Application research of robust LS-SVM regression model in forecasting patent application counts",

abstract = "A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.",

keywords = "Cross-validation algorithm, Forecasting, Patent application count, Support vector machine",

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AU - Zhang, Li Wei

AU - Zhang, Qian

AU - Wang, Xue Feng

AU - Zhu, Dong Hua

PY - 2009/12

Y1 - 2009/12

N2 - A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

AB - A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

KW - Cross-validation algorithm

KW - Forecasting

KW - Patent application count

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/75349092827

M3 - Article

AN - SCOPUS:75349092827

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VL - 18

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JO - Journal of Beijing Institute of Technology (English Edition)

JF - Journal of Beijing Institute of Technology (English Edition)

IS - 4

ER -

Application research of robust LS-SVM regression model in forecasting patent application counts

摘要

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引用此