On-line methods in machine learning

建议的研究集中在随机环境中的在线学习和顺序决策。顺序决策问题的例子比比皆是：控制工业厂房，进行临床试验，管理投资，监测管道系统等在线学习算法提高了系统的性能在其正常运行。相比之下，离线学习算法使用固定量的数据进行训练，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个潜在的弱点，因为它们从不停止适应。

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.