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基于大数据分析的机器学习算法探讨 被引量:3

Discussion of Machine Learning Algorithm Based on Big Data Analysis
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摘要 大数据重视数据的加工处理,以保障数据有效增值。随着云时代的快速发展,大数据覆盖范围逐步扩大,受到社会各界的广泛关注。现代社会发展过程中,大数据分析逐步应用于企业未来发展规划、风险评价和市场发展现状整合等方面。随着社会诸多领域的快速发展,信息流通量逐步扩大,互联网发展更加迅速,促使大数据逐步应用于各个领域。 Big data attaches importance to data processing to ensure the effective value-added of data. With the rapid development of the cloud era, the coverage of large data has gradually expanded, which has attracted wide attention from all walks of life. In the process of modern social development, big data analysis is gradually applied to the future development planning of enterprises, risk assessment and integration of market development status. With the rapid development of many fields of society, the information flow is gradually expanding, and the development of the Internet is more rapid, which promotes the gradual application of big data in various fields.
作者 王硕 Wang Shuo(Hebei Branch of National Computer Network and Information Security Management Center, Shijiazhuang Hebei 050000, China)
出处 《信息与电脑》 2019年第4期59-60,共2页 Information & Computer
关键词 大数据分析 机器学习算法 互联网 big data analysis machine learning algorithm Internet
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  • 1Nature. Big data [EB/OL]. [ 2012-10-02 ]. http://www. nature, com/news/Specials/bigdata/index, html.
  • 2Science. Special online collection: Dealing with data [EB/OL]. [2012-10-02]. http: //www. sciencemag, org/site/ speclal/data.
  • 3杨海钦,吕荣聪,金国庆.面向大数据的在线学习算法[J].中国计算机学会通讯.2014,10(11):36-40.
  • 4Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65(6): 386-408.
  • 5Crammer K, Dekel O, Keshet J, et al. Online passive- aggressive algorithms [J]. Journal of Machine Learning Research, 20061 7(3): 551-585.
  • 6Langford J, Li Lihong, Zhang Tong. Sparse online learning via truncated gradient [J]. Journal of Machine Learning Research, 2009, 10(3) : 777-801.
  • 7Duchi J, Singer Y. Efficient online and batch learning using forward backward splitting [J]. Journal of Maching Learning Research, 2009, 10(12): 2899-2934.
  • 8Xiao L. Dual averaging methods for regularized stochastic learning and online optimization [J]. Journal of Machine Learning Research, 2010, 11(10): 2543-2596.
  • 9Yang Haiqin, Lyu M R, King I. Efficient online learning for multi-task feature selection [J]. ACM Trans on Knowledge Discovery from Data, 2013, 1(1) ; 1-28.
  • 10Yang Haiqin, King I, Lyu M R. Sparse Learning under Regularization Framework: Theory and Applications [M]. 1st ed. Saarbrucken, Germany: LAP Lambert Academic Publishing, 2011.

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