e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Title

Study on Supply Chain Partner Selection Based on Support Vector Machine

en
Abstract

SVM is a novel machine learning technique developed on empirical risk minimization principle. SVM has many advantages in solving small sample size, nonlinear and high dimensional pattern recognition problem. Based on the study of SVM, this paper discusses its application in the supply chain partner selection that provides a reference for enterprise to select the partner.

en
Year
2012
en
Country
  • CN
Organization
  • Zhejiang_Normal_Univ (CN)
Data keywords
  • machine learning
en
Agriculture keywords
  • supply chain
en
Data topic
  • big data
  • information systems
  • modeling
en
SO
FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.