Flexible Classification and Regression

灵活的分类和回归

• 批准号: 0505432
• 负责人: Ji Zhu
• 金额: --
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0505432&HistoricalAwards=false
• 关键词: Flexible; Classification; Regression

The research aims to combine statistical and computational considerations in designing new and useful predictive modeling tools and algorithms. Specifically, the research involves the development of: a) new statistically motivated multi-class boosting algorithms, based on a family of multi-class loss functions and forward stagewise additive modeling; b) a family of (loss, penalty) pairs that give piecewise linear solution paths, and yield modeling tools for both regression and classification which are robust, adaptable and efficient; c) a general theory and efficient algorithms for solving an L1 regularized problem in infinite dimensional predictor space.With the advent of modern technologies, the needs for predictive modeling tools have been increasing rapidly. Consequently, many new ideas and methods have been finding their way into the statistical community in recent years. These are mainly related to the design and analysis of useful techniques for modeling of high dimensional, noisy data, and these techniques are now being applied to bioinformatics, high energy physics, speech recognition, text mining, and a wide range of other important practical problems. This research aims to push these developments forward along the line of regularization in predictive modeling, and is expected to have broader impacts on the practice and education in the domains of statistics, machine learning and data mining.

该研究旨在结合统计和计算的考虑，设计新的和有用的预测建模工具和算法。具体来说，研究包括：a)基于一系列多类损失函数和前向阶段加性建模的新的统计激励多类增强算法；B)一组（损失，惩罚）对，给出分段线性解路径，并产生回归和分类的建模工具，这些工具具有鲁棒性，适应性强且高效；c)求解无限维预测空间中L1正则化问题的一般理论和有效算法。随着现代技术的出现，对预测建模工具的需求迅速增加。因此，近年来，许多新的思想和方法已经进入统计界。这些主要与设计和分析高维、噪声数据建模的有用技术有关，这些技术现在被应用于生物信息学、高能物理、语音识别、文本挖掘和其他广泛的重要实际问题。本研究旨在沿着预测建模的正则化方向推动这些发展，并有望对统计学、机器学习和数据挖掘领域的实践和教育产生更广泛的影响。