NSF Young Investigator: Understanding Basic Issues in Machine Learning, On-line Planning, and Approximate Algorithms

本研究探讨了理论工具的应用， 分析机器学习中的基本问题，从不完整的 信息和近似算法。 在机器学习领域， 一个方向是提供用于快速学习的新算法技术， 有很多关于物体的信息，但只有一小部分 是真正相关的。 这些技术对于超速驾驶和 学习和自动化选择信息的过程， 给出了一个学习算法。 还有用于学习的新算法 神经网络的研究，特别是在困难的情况下，高 维输入空间。 审查的其他基础研究问题 包括学习和密码学之间的关系，以及 学习者可以有效地采用更积极的方法。 领域的 在线规划过去的工作一直是在没有地图的情况下旅行的策略 在具有简单类型障碍物的场景中，其性能可以得到保证 如果有一张该地区的地图，它不会比最好的情况差太多。 这一点已扩大到更多不同的情况，包括一些人 有多种局部地图可供使用。 一个有趣的例子是， 地图是由先前的探索所创建的。 在这里，人们可以将以下联合收割机的想法结合起来： 在线规划和学习。

This research investigates application of theoretical tools to analyze fundamental issues in machine learning, in planning from incomplete information, and in approximation algorithms. In the area of machine learning, one direction is to provide new algorithmic techniques for fast learning when there is much information available about objects but only a small amount is truly relevant. These techniques are potentially useful both for speeding up learning and for automating the process of selecting what information is given to a learning algorithm. Also new algorithms for learning using neural networks are investigated, especially in the difficult case of high dimensional input spaces. Other fundamental research issues examined include relationships between learning and cryptography, and various ways a learner can usefully employ more active approaches. In the domain of on-line planning past work has been on strategies for traveling without a map in scenes with simple types of obstacles, whose performance can be guaranteed to be not too much worse than the best possible, if one had a map of the region. This is expanded to more varied situations, including those where some kinds of partial maps are available. One interesting case is where partial maps are created by previous explorations. Here one can combine ideas of on-line planning and learning.