Approximating Optimal Solutions Using Polynomial-Time Learning Machines and Applications in Intelligent Network Management

这个职业研究项目的方法是在机器学习系统中应用弱分类器来产生更好的学习规则。弱分类器是那些不允许始终正确识别概念的分类器，但它比随机猜测工作得更好。单独应用它们还不够好，但结合起来，它们可能是有用的。有两个主要考虑因素--学习效率和泛化误差。在这种方法下，本研究的目的是严格研究学习系统在监督学习性能和效率方面的基本问题。(2)将自适应学习机应用于智能网络管理。

The approach of this Career Research Project is to apply weak classifiers in machine learning systems to produce better learning rules. Weak classifiers are those that do not allow correct concept identification all the time, but that work better than random guesses. Alone, they are not good enough for applications, but in combinations, they may be useful. There are two major considerations - efficiency of learning and generalization error. Within this approach, the goal of this research is to rigorously investigate the fundamental problems of learning systems in supervised learning performance and efficiency (2) to apply adaptive learning machines to intelligent network management.