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

分类和回归是信息技术时代提出的两个基本的统计问题，同时也得到了它所提供的计算工具的帮助。 Boosting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，可以满足我们IT时代对海量数据集的需求。研究人员和她的同事研究了Boosting和SVM何时以及为什么工作，以阐明Boosting和SVM的设计或调整;特别是，了解L2 Boosting中基础学习者的选择以及输入密度分布对SVM中内核引起的再生内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，在基于多角度成像光谱辐射计（MISR）数据的冰/雪云检测问题中，使用对助推和SVM的理论理解。 研究人员和她的同事们还开发了一种基于线性相关性的新型云检测算法，使用MISR数据，并将其与基于boosting和SVM的方法进行比较。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带来了巨大的机遇，既带来了基本的方法学挑战，也带来了具有现实影响的应用。 数据收集和计算技术的进步导致遥感数据等海量数据集的激增。了解统计在这种数据丰富的应用中的作用，迫使我们重新评估和修订传统的程序和框架。研究人员及其同事研究了Boosting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，可满足我们IT时代海量数据集的需求。他们与NASA喷气推进实验室的同事合作，将这些研究的研究结果应用于云检测问题，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。 多角度成像光谱辐射计（MISR）是由美国宇航局于1999年发射的，提供9个角度（4个波段）的数据来反演或估计云的高度，从而云检测。然而，即使使用MISR数据，云检测也已被证明在冰/雪上非常困难。研究人员和她的同事开发了一种基于线性相关的新型云检测算法，使用MISR数据来处理冰/雪，并将其与基于提升和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.