Learning Algorithms for Complex Data

复杂数据的学习算法

• 批准号: 9877049
• 负责人: Leslie Valiant
• 金额: $ 33.14万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9877049&HistoricalAwards=false
• 关键词: Learning; Algorithms; Complex; Data

Machine learning is now a well developed field that enables complex data sets to be analyzed with a new effectiveness. This research is concerned with further developing the fundamental algorithmic components of machine learning so as to extend the reach of these methods to new applications, particularly those of a cognitive nature where many complex tasks need to be learned simultaneously from a limited data set. Emphasis will be placed on techniques that allow learning from a few examples, and those that allow learning of multi-object relations. The mistake-bounded model of machine learning has provided some powerful techniques for showing that certain knowledge representations, such as linear threshold functions, can be learned attribute-efficiently. The task remains, however, to classify more precisely the known representation classes according to this property. On the subject of learning relations, a self-evident challenge is to control the computational complexity of this task. However, this is subsumed by the more general question of how exactly one should define this problem in order that the algorithmic techniques of machine learning can be brought to bear effectively on it.

机器学习现在是一个成熟的领域，可以以新的效率分析复杂的数据集。 这项研究涉及进一步开发机器学习的基本算法组件，以便将这些方法的范围扩展到新的应用，特别是那些需要从有限的数据集中同时学习许多复杂任务的认知性质的应用。 重点将放在允许从几个示例中学习的技术以及允许学习多对象关系的技术上。 机器学习的错误界限模型提供了一些强大的技术来表明某些知识表示（例如线性阈值函数）可以有效地学习属性。 然而，任务仍然是根据这个属性对已知的表示类别进行更精确的分类。 在学习关系的主题上，一个不言而喻的挑战是控制该任务的计算复杂性。 然而，这包含在一个更普遍的问题中，即应该如何准确地定义这个问题，以便机器学习的算法技术可以有效地发挥作用。