Flexible Machine Learning

下一代机器学习系统需要比当前系统更灵活。机器学习系统需要能够根据需要增长新的结构，考虑从关系知识中产生的重复子结构，处理抽象层次结构，并更优雅地科普异构数据。该项目解决了这些问题。它旨在解决机器学习的贝叶斯方法（特别是图形模型）和机器学习的频率论方法（特别是内核机器）中的问题。在图形模型设置中，PI描述了一种新的结构学习方法，该方法基于灵活的先验知识，称为中国餐馆过程（CRP）。“它探索了CRP的泛化，称为分层狄利克雷过程”，这使得考虑重复或部分重复的子结构成为可能。它还提出了探索的CRP，被称为嵌套的中国餐馆的过程”学习抽象层次的泛化。在内核机器的区域，PI建立在他以前的NSF赞助的工作，考虑基于凸优化，特别是半定规划的工具，结合异构内核的方法。他将使用这些想法来定义新的特征选择方法，并为半定规划方法设计新的算法，这些算法类似于二次规划的序列最小优化（SMO）算法，这些算法允许支持向量机的崛起。该项目将集中于推动信息检索，生物信息学，计算机程序中的错误发现和传感器网络等领域的应用。

The next generation of machine learning systems will need to be substantially more flexible than current systems. Machine learning systems will need to be able to grow new structure as needed, to take into account repeated substructures that arise from relational knowledge, to deal with abstraction hierarchies, and to cope more gracefully with heterogeneous data. This project addresses these issues. It aims at problems both in the Bayesian approach to machine learning (specifically, graphical models) and the frequentist approach to machine learning (specifically, kernel machines). In the graphical model setting, the PI describes a new approach to structure learning based on a flexible prior known as the Chinese restaurant process (CRP)." It explores generalization of the CRP referred to as the hierarchical Dirichlet process" that makes it possible to take into account repeated or partially-repeated sub-structures. It also presents explores a generalization of the CRP that referred to as the nested Chinese restaurant process" for learning abstraction hierarchies.In the area of kernel machines, the PI builds on his previous NSF-sponsored work to consider methods for combining heterogeneous kernels based on tools from convex optimization, in particular semidefinite programming. He will use these ideas to define novel feature selection methods, and to design new algorithms for the semidefinite programming approach that are the analog of the sequential minimal optimization" (SMO) algorithm for quadratic programming that have permitted the rise to prominence of the support vector machine. The project will focus on driving applications in the areas of information retrieval, bioinformatics, bug-finding in computer programs, and sensor networks.