Learning with Dependent Data: With Applications in Computational Genome Analysis

使用相关数据进行学习：在计算基因组分析中的应用

• 批准号: 225910935
• 负责人: Professor Dr. Marius Kloft
• 金额: --
• 依托单位: Courant Institute of Mathematical Sciences
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/225910935?language=en
• 关键词: Learning; Dependent; Data; Applications; Computational

The classical machine learning theory is built upon the assumption of independent and identically distributed random variables. In practical applications, however, this assumption is often violated, for instance, when the data exhibits temporal or spatial correlations. In the proposed program, we will contribute to the theory of learning with dependent data, the development of efficient and effective learning machines, and the application thereof to computational genome analysis.

经典的机器学习理论是建立在独立和同分布随机变量的假设之上的。然而，在实际应用中，这一假设经常被违反，例如，当数据显示出时间或空间相关性时。在该计划中，我们将为依赖数据学习理论，高效学习机器的开发以及其在计算基因组分析中的应用做出贡献。