Flexible Classification and Regression

本研究旨在结合联合收割机的统计和计算的考虑，设计新的和有用的预测建模工具和算法。 具体而言，该研究涉及以下方面的开发：a）基于多类损失函数族和前向阶段加性建模的新的统计激励的多类提升算法; B）基于多类损失函数族和前向阶段加性建模的多类提升算法。（损失，惩罚）对，给出分段线性解决方案路径，并产生用于回归和分类的建模工具，其是鲁棒的、适应性强的和有效的;（3）无穷维预测空间中L1正则化问题的一般理论和有效算法。随着现代技术的发展，对预测建模工具的需求迅速增加。 因此，近年来，许多新的想法和方法已经进入统计界。 这些主要涉及到设计和分析有用的技术建模的高维，嘈杂的数据，这些技术现在被应用到生物信息学，高能物理，语音识别，文本挖掘，以及广泛的其他重要的实际问题。 本研究旨在推动这些发展沿着预测建模的正则化路线，并有望在统计，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。

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.