Some Practical Issues in Computational Learning Theory

本研究的目的是使一大批机器学习算法在实践中更有用。这项研究的重点是机器学习的正式模型，这是一个被称为计算学习理论的领域。该领域的总体目标是给出学习问题的形式化数学定义，并提供有效的算法来解决这些问题。计算学习理论社区已经提出了大量的算法来解决低级的归纳问题，但是如果要在现实世界的软件中使用这些学习算法，它们必须具有某些属性。大型人工智能系统的构建者等从业者抱怨说，这些算法需要太多的数据才能得出结论，而且对数据中的噪声过于敏感。该项目将试图证明，在平均情况下所需的数据比理论家对最坏情况的估计要少得多，并研究如何修改这些算法以抵抗噪声。提议的项目包括算法的设计和分析，本质上是数学性质的。然而，如果成功，这个项目应该允许最近开发的机器学习算法的实际应用。

The objective of this research is to make a large group of machine learning algorithms more useful in practice. This research focuses on formal models of machine learning, an area known as computational learning theory. The overall goal of this field is to give formal, mathematical definitions of learning problems, and to provide efficient algorithms to solve these problems. The computational learning theory community has proposed a large number of algorithms for solving low-level induction problems, but there are certain properties such learning algorithms must have if they are going to be used in real-world software. Practitioners, such as builders of large artificial intelligence systems, have complained that these algorithms require too much data to reach conclusions, and are too sensitive to noise in the data. This project will attempt to prove that much less data is necessary for average cases than the estimates given by theorists for the worst case, and to study how such algorithms can be modified to resist noise. The proposed project consists of the design and analysis of algorithms, and is essentially mathematical in nature. Nevertheless, if successful, this project should allow the practical application of recently developed machine learning algorithms.