Kernels and ensembles for horizontal and vertical information selection

用于水平和垂直信息选择的内核和集成

• 批准号: 250419-2011
• 负责人: Zhu, Mu
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508820
• 关键词: Kernels; ensembles; horizontal; vertical; information

Exemplified by the support vector machine and the AdaBoost algorithm, kernel methods and ensemble methods constituted much of the new research in statistical machine learning during the last decade. My proposed research will focus on the development of these two classes of methods for two very general data-analytic problems, which I call horizontal and vertical information selection.

以支持向量机和AdaBoost算法为例，核方法和集成方法构成了过去十年中统计机器学习的许多新研究。我建议的研究将集中在两个非常一般的数据分析问题，我称之为横向和纵向信息选择这两类方法的发展。