Learning Algorithms for Complex Data

机器学习现在是一个发展良好的领域，它使复杂的数据集能够以新的有效性进行分析。本研究关注的是进一步发展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中，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。重点将放在那些允许从几个例子中学习的技术，以及那些允许学习多对象关系的技术。机器学习的错误边界模型提供了一些强大的技术来显示某些知识表示，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这项任务的计算复杂性。然而，这被包含在一个更普遍的问题中，即人们应该如何准确地定义这个问题，以便机器学习的算法技术能够有效地发挥作用。

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.