CISE Research Resources: Instrumentation for Experimental Research in Machine Learning, Collaborative Filtering, and Virtual Environments

EIA 0224012 Dietterich，Thomas G.Herlocker，Jonathan L.Metoyer，Ronald Oregon State University标题：CEISRR：机器学习、协作过滤和虚拟环境实验研究的仪器该项目升级当前集群以用于大型预计算以实现有效的实时性能，支持服务于以下四个项目的设备：用于沉浸式训练环境的实时人物动画、用于协作过滤(CF)的概率推荐方法、用于空间和序列数据的机器学习、第一个项目的目的是自动选择和排序捕获的运动短序列，以沿轨迹驱动角色，同时保持序列之间看起来自然的过渡。该项目利用离线强化学习(动态规划)算法来预先计算在一系列轨迹约束下在任意一对姿势之间移动的策略。然后允许对字符的实时控制，而不需要大量的运行时搜索。第二个项目使用协作过滤(CF)系统，该系统可以预测项目评级的概率分布。目前，CF是一种多个计算机用户间接帮助彼此做出决策和确定问题解决方案的方法，它提供评级或选票，以较高的票数返回项目，而不是附加概率。该设备将通过测试新算法的未优化原型实现来加快研究进程，从而允许在不延迟人工优化的情况下评估拟议的算法。第三个项目涉及机器学习的新兴应用(例如，计算机入侵检测、从网页中提取信息、遥感)，这些应用涉及时间、顺序或空间数据，其中附近的数据点通常是相关的。PI已经开发了条件随机场(CRF)的顺序分析方法的并行实现，该方法在计算时间上提供近线性加速，但在可处理的数据集的大小方面受到限制。集群升级应该产生更快的解决方案，并允许考虑更大的数据集。最后一个项目，用两种新的强化学习算法进行实验，只要可达状态的数量足够小，足以容纳在主内存中，就可以扩展到大的搜索空间。一种方法将线性规划与支持向量机技术相结合，另一种方法将梯度下降搜索与动态规划相结合。这些方法需要昂贵的离线计算，导致可应用于运行时搜索的高效启发式方法。此外，学生还将在一门并行计算课程中使用该集群。

EIA 0224012Dietterich, Thomas G.Herlocker, Jonathan L.Metoyer, Ronald Oregon State University Title: CISE RR: Instrumentation for Experimental Research in Machine Learning, Collaborative Filtering, and Virtual Environments This project, upgrading the current cluster for use in large pre-computations that enable effective real-time performance, supports equipment servicing the following four projects:Real Time Animation of Human Characters for Use in Immersive Training Environments,Probabilistic Recommendation Methods for Collaborative Filtering (CF),Machine Learning for Spatial and Sequential Data, andReinforcement Learning for Learning Search Control Heuristics with Applications to Protein Structure Determination from Nuclear Magnetic Resonance Spectroscopy and to Real-Time Animation.The first project aims at automatically selecting and ordering short sequences of captured motion to drive a character along a trajectory while maintaining naturally looking transitions between the sequences. The project exploits off-line reinforcement learning (dynamic programming) algorithms to pre-compute a policy for moving between any pair of poses under a range of trajectory constraints. Real-time control of characters is then permitted without the need for large run-time searches. The second project utilizes collaborative filtering (CF) systems that can predict a probability distribution over the rating of an item. Currently CF, a method by which multiple computer users indirectly help each other make decisions and identify solutions to problems, provides ratings or votes returning the items with the higher number of votes, rather than attaching a probability. The equipment will speed the research process by enabling the testing of un-optimized prototype implementations of new algorithms, and hence allow the evaluation of proposed algorithms without the delay of manual optimization. The third project deals with emerging applications of machine learning (e.g., computer intrusion detection, information extraction from web pages, remote sensing) involving temporal, sequential, or spatial data where nearby data points are typically correlated. The PIs have developed a parallel implementation of a sequential analysis method for conditional random fields (CRFs) that gives near-linear speedups in the computation time, but which is limited in the size of data sets that can be processed. The cluster upgrade should yield a faster solution and allow consideration of larger data sets. The last project, experiments with two new algorithms for reinforcement learning that scale to large search spaces as long as the number of reachable states is small enough to fit in main memory. One method combines linear programming with support-vector machine techniques; the other combines gradient descent search with dynamic programming. These methods, requiring an expensive off-line computation, result in an efficient heuristic that can be applied to the run-time search. Moreover, the cluster will be used by students in a parallel computation course.