从计算生物学到搜索引擎，机器学习算法已经成为计算机科学中占据主导地位、并且无处不在的强大工具。统计学习理论不但能对现有学习算法的性质提供理论分析，也能用于推导新的或者改进的学习算法。从神经网络、支撑向量机(SVM)到boosting，margin理论都起到了关键性的作用。因此本项目拟从如下三个方面，对margin理论进行深入研究：(1) 基于margin分布的boosting泛化界，(2) PAC-贝叶斯框架下线性分类器（如SVM）的margin界，(3) 主动学习算法中margin对标签复杂度的作用。本项目将以margin理论为研究主线，紧密联系多种学习算法，并通过实验来验证理论的正确性，力争在统计学习理论方面取得突破与创新。

From computational biology to search engine problems, machine learning algorithms have emerged as the dominant and ubiquitous tool in computer science. Statistical learning theory can not only provide insights to the performance of existing learning algorithms, but also help design new or improved algorithms. From neural networks to support vector machines (SVM) and boosting, the margin theory plays a key role in the design and the analysis of these algorithms. This project aims at the study of margin theory from the three aspects: (1) Generalization bound for boosting based on the margin distribution, (2) PAC-Bayesian margin bound for linear classifiers like SVM, (3) the effects of margin on the label complexity for active learning algorithms. The main clue of this project is the margin theory, which is applied to several learning algorithms and is corroborated through extensive experiments. Our goal is to seek ground breakthroughs and innovative ideas in statistical learning theory.