We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that takes into account the average quality of the voters and their average disagreement. We also propose an extensive PAC-Bayesian analysis that shows how the C-bound can be estimated from various observations contained in the training data. The analysis intends to be self-contained and can be used as introductory material to PAC-Bayesian statistical learning theory. It starts from a general PAC-Bayesian perspective and ends with uncommon PAC-Bayesian bounds. Some of these bounds contain no Kullback-Leibler divergence and others allow kernel functions to be used as voters (via the sample compression setting). Finally, out of the analysis, we propose the MinCq learning algorithm that basically minimizes the C-bound. MinCq reduces to a simple quadratic program. Aside from being theoretically grounded, MinCq achieves state-of-the-art performance, as shown in our extensive empirical comparison with both AdaBoost and the Support Vector Machine.

针对多数表决在二元分类中的行为，提出了一种风险界定——C-bound，考虑到选民的平均质量和平均分歧，结合具有自我包容性的PAC-Bayesian分析可从训练数据中估计C-bound，最终提出基于C-bound最小化的MinCq算法，达到与AdaBoost和SVM相当的效果。

多数投票的风险界限：从PAC-Bayesian 分析到 学习算法

多数投票的风险界限：从PAC-Bayesian 分析到学习算法