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

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.

EIA 0224012DIETTERICH，THOMAS G.HERLOCKER，JONATHAN L.METOYER，RONALD OREGON州立大学标题：CISE RR：机器学习，实验性研究仪器，该项目的机器学习，合作环境和虚拟环境的实验性研究，该项目为当前的项目升级，以实现实时效果，以实现实时效果：沉浸式培训环境，用于协作过滤的概率建议方法（CF），用于空间和顺序数据的机器学习，用于学习搜索搜索控制启发式的AndreInforkection学习，并应用于蛋白质结构从核磁共振光谱和实时动画中进行蛋白质结构确定。序列。该项目利用离线加固学习（动态编程）算法，以预先计算在一系列轨迹约束下任何一对姿势之间移动的策略。然后允许对字符的实时控制，而无需大量的运行时搜索。第二个项目利用协作过滤（CF）系统可以预测项目评级的概率分布。当前CF是一种方法，通过这种方法，多个计算机用户间接帮助彼此做出决策并确定问题的解决方案，并提供了返回项目的评级或投票，而不是附加概率。该设备将通过实现新算法的未优化原型实现的测试来加快研究过程，从而允许评估所提出的算法，而无需延迟手动优化。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）涉及时间，顺序或空间数据，这些数据通常与附近的数据点相关。 PI已开发了用于条件随机字段（CRF）的顺序分析方法的并行实现，该方法在计算时间内给出了近乎线性的加速，但在可以处理的数据集的大小上受到限制。集群升级应产生更快的解决方案，并允许考虑较大的数据集。最后一个项目，使用两种新算法进行强化学习的实验，只要可到达状态的数量足够小以适合主内存，就可以扩展到大型搜索空间。一种方法将线性编程与支持矢量机技术结合在一起；另一个结合了梯度下降搜索与动态编程。这些方法需要昂贵的离线计算，从而导致可以应用于运行时搜索的有效启发式。此外，学生将在平行计算课程中使用集群。