On-line methods in machine learning

本文的研究重点是随机环境下的在线学习和序贯决策。顺序决策问题的例子比比皆是：控制工业工厂、进行临床试验、管理投资、监控管道系统等。在线学习算法提高了系统在正常运行期间的性能。相比之下，脱机学习算法使用固定数量的数据进行训练，然后在关闭学习的情况下使用。在离线学习中，训练数据必须完全代表系统的行为，否则性能可能很差。在线学习系统克服了这一潜在的弱点，因为它们从未停止适应。

The proposed research is focused on on-line learning and sequential decision making in stochastic environments. Examples of sequential decision making problems abound: controlling industrial plants, performing clinical trials, managing investments, monitoring pipeline-systems, etc. An on-line learning algorithm improves the performance of a system during its normal operation. In contrast, off-line learning algorithms are trained with a fixed amount of data and then used with learning turned off. In off-line learning the training data has to be fully representative of the system's behaviour or performance may be poor. On-line learning systems overcome this potential weakness because they never stop adapting.