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农民科技培训体系研究 被引量:3

A Research on Farmers Technology Training System
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摘要 发展现代农业,农民的科技素质、市场意识十分关键。依据江苏省农民科技学习调查问卷,对苏南、苏中、苏北地区进行随机调查。针对调查数据的特点,运用分形维构建特征数据集的方法将数据集进行属性约简,依据舒尔茨人力资源管理理论和实证数据结果分析,提出进一步完善我国农民科技培训的体系,以切实提高农民的科技素质,为推进我国实现农业发展方式的转变提供有益参考。 Scientific and technological quality of farmers and market awareness are critical to develop modern agriculture.This paper designed farmers learning technology questionnaires of Jiangsu Province and made random survey in Southern,Central and Northern region of Jiangsu.This paper use the construction of the fractal dimension characteristics of data sets,and set attribute reduction of data in accordance with the characteristics of survey data.This paper suggests further improving the system of farmers' technology training based on Schultz human resource management theory and analysis of empirical data in order to raise the scientific and technological quality of farmers,hoping to raise useful reference for promoting the transformation of agricultural development mode.
机构地区 金陵科技学院
出处 《科技与经济》 CSSCI 2011年第3期50-54,共5页 Science & Technology and Economy
基金 江苏省社会科学基金项目--"现代农民科技学习状况研究"(项目编号:08TQB007项目负责人:崔斌)成果之一
关键词 分形维 科技培训体系 农民专业合作社 the fractal dimension the system of technology training the agricultural specialized cooperatives
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  • 1葛维威.新型农民培育视角下的农村职教改革[J].职业技术教育,2006,27(22):52-54. 被引量:1
  • 2徐庆国,黄丰.关于新型农民培训工作的思考[J].湖南农业大学学报(社会科学版),2007,8(1):47-49. 被引量:26
  • 3毛泽东.毛泽东选集(第三卷)[M].北京:人民出版社,1997:1013.
  • 4列宁.列宁全集(第33卷)[M].北京:人民出版社,1964:422-429.
  • 5Talavera L. Feature selection as a preproceasing step for hierarchical clusterking[A]. In: Bratko I. Proc of the 16th Conf on Machine Learning[C]. Bled: AAAI Press,1999. 389 - 397.
  • 6Scherf M, Brauer W. Improving RBF networks by the feature selection appraach EUBAFES[A]. In: Gersmer W.Proc 7th Int Conf on Artificial Neural Networks [C].Lausanne: Springer, 1997.391 - 396.
  • 7Robert A, Stocker E. Classification and feature selection by a self-organizing neural network[A]. In:Dorffner G. Proc of Int Conf on Artificial and Neural Networks[ C ].Edinburgh: Springer, 1999. 651 - 660.
  • 8Pernkopf F, O'Leary P. Feature selection for classification using genetic algorithms with a novel encoding [A]. In:Skarbek W. Proc of Computer Analysis of Images and Patterns[C]. Warsehau: Springer, 2001. 161 - 168.
  • 9Boussouf M, Qudafou M. Sealable feature selection using rough set theory[A]. In:Ziarko W. Proc of 2nd Int Conf on Rough Sets and Current Trends in Computing [ C ].Banff: Springer, 2000.131 - 138.
  • 10Traina C, Traina A, Wu L, et al. Fast feature selection using fractal dimension[ A ]. In: Faloutsos C. Proc of XV Brazilian Symposium on Databases [C]. Paraila: Springer,2000.78 - 90.

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