Boosting, Support Vector Machines, and Cloud Detection over Ice and Snow

分类和回归是信息技术（IT）时代提出的两个基本统计问题，同时也得到了它所提供的计算工具的帮助。增强和支持向量机（svm）是机器学习中用于回归和分类的两种革命性方法，它们满足了我们IT时代海量数据集的需求。研究者和她的同事研究何时以及为什么增强和支持向量机起作用，以阐明增强和支持向量机的设计或调优；特别是了解L2Boosting中基础学习器的选择，以及输入密度分布对SVM中核诱导的再现核希尔伯特空间的影响。然后，与喷气推进实验室的同事合作，将助推和支持向量机的理论知识用于基于多角度成像光谱仪（MISR）数据的冰雪云层探测问题。研究者和她的同事们还利用MISR数据开发了一种基于线性相关性的新型云检测算法，并将其与基于boosting和SVM的方法进行了比较。信息技术（IT）正在改变我们生活的方方面面。在专业层面上，IT领域的新兴创新为统计研究提供了巨大的机会，既提供了基本的方法挑战，也提供了具有实际影响的应用程序。数据收集和计算技术的进步导致大量数据集的激增，例如来自遥感的数据集。理解统计在这些数据丰富的应用程序中的作用迫使我们重新评估和修改传统的程序和框架。研究者及其同事研究了增强和支持向量机（svm），这是机器学习中用于回归和分类的两种革命性方法，满足了我们IT时代海量数据集的需求。他们与美国宇航局喷气推进实验室的同事合作，将这些研究成果应用于云探测问题，这是任何气候预测或建模（包括天气预报和全球变暖监测）的关键一步。多角度成像光谱仪（MISR）于1999年由美国宇航局发射，提供9角度（4波段）数据来检索或估计云高度，从而进行云检测。然而，即使使用MISR数据也很难在冰雪上进行云探测。研究者和她的同事开发了一种新的基于线性相关性的云检测算法，使用MISR数据在冰/雪上工作，并将其与基于boosting和SVM的方法进行比较。

DMS-0306508Bin YuBoosting, support vector machines, and cloud detection over ice and snowAbstractClassification and regression are two fundamental statistical problems posed by the Information Technology (IT) age and at the same time aided by the computational tools it provides. Boosting and Support Vector Machines (SVMs) are two revolutionary methodologies from machine learning for regression and classification that meet the needs of massive data sets of our IT age. The investigator and her colleagues study when and why boosting and SVMs work to shed light on the design or tuning of Boosting and SVMs; in particular, understanding on the choice of the base learner in L2Boosting and the effect of the input density distribution on the Reproducing Kernel Hilbert space induced by the kernel in an SVM. Theoretical understandings on boosting and SVMs are then used, in collaboration with colleagues at Jet Propulsion Laboratory, in the cloud-detection problem over ice/snow based on Multi-angle Imaging SpectroRadiometer (MISR) data. The investigator and her colleagues also develop a novel cloud detection algorithm based on linear correlations using MISR data and compare this with the boosting and SVM based approaches.Information Technology (IT) is changing just about every facet of our lives. At a professional level, emerging innovations in IT areas represent tremendous opportunities for statistical research, providing both fundamental methodological challenges as well as applications with real-world impact. Advances in data collection and computing technologies have led to the proliferation of massive data sets such as those from remote sensing. Understanding the role of statistics in such data-rich applications forces us to reevaluate and revise traditional procedures and frameworks. The investigator and colleagues study Boosting and Support Vector Machines (SVMs), which are two revolutionary methodologies from machine learning for regression and classification that meet the needs of massive data sets of our IT age. In collaboration with colleagues at NASA's Jet Propulsion Laboratory, they apply the research results from these studies to the problem of cloud detection, which is a crucial step in any climate prediction or modeling including weather forecasting and global warming monitoring. Multi-angle Imaging SpectroRadiometer (MISR) was launched in 1999 by NASA to provide 9 angle (4 band) data to retrieve or estimate the cloud height and hence cloud detection. However, cloud detection even with MISR data has been proven very difficult over ice/snow. The investigator and her colleagues develop a novel cloud detection algorithm based on linear correlations using MISR data to work over ice/snow and compare it with boosting and SVM based approaches.