We study the online variant of GentleAdaboost, where we combine a weak
learner to a strong learner in an online fashion. We provide an approach to
extend the batch approach to an online approach with theoretical justifications
through application of line search. Finally we compare our online boosting
approach with other online approaches across a variety of benchmark datasets.

我们研究了在线版本的 GentleAdaboost，通过在线方式将弱学习器与强学习器相结合。我们提供了一种将批量方法扩展为在线方法的途径，并通过线搜索的应用进行了理论上的证明。最后，我们将我们的在线增强方法与其他在线方法在各种基准数据集上进行了比较。

在线 GentleAdaBoost -- 技术报告

Online GentleAdaBoost -- Technical Report

We study online boosting, the task of converting any weak online learner into
a strong online learner. Based on a novel and natural definition of weak online
learnability, we develop two online boosting algorithms. The first algorithm is
an online version of boost-by-majority. By proving a matching lower bound, we
show that this algorithm is essentially optimal in terms of the number of weak
learners and the sample complexity needed to achieve a specified accuracy. This
optimal algorithm is not adaptive however. Using tools from online loss
minimization, we derive an adaptive online boosting algorithm that is also
parameter-free, but not optimal. Both algorithms work with base learners that
can handle example importance weights directly, as well as by rejection
sampling examples with probability defined by the booster. Results are
complemented with an extensive experimental study.

研究在线提升的两种算法：boost-by-majority 和自适应在线提升算法，证明其在可接受精度下基本上是最优的。两种算法都能处理样本的重要性权重，并且使用了在线损失最小化技术。

在线 boosting 的最优与自适应算法

Optimal and Adaptive Algorithms for Online Boosting

We study the task of online boosting--combining online weak learners into an
online strong learner. While batch boosting has a sound theoretical foundation,
online boosting deserves more study from the theoretical perspective. In this
paper, we carefully compare the differences between online and batch boosting,
and propose a novel and reasonable assumption for the online weak learner.
Based on the assumption, we design an online boosting algorithm with a strong
theoretical guarantee by adapting from the offline SmoothBoost algorithm that
matches the assumption closely. We further tackle the task of deciding the
number of weak learners using established theoretical results for online convex
programming and predicting with expert advice. Experiments on real-world data
sets demonstrate that the proposed algorithm compares favorably with existing
online boosting algorithms.

本文研究了在线增强学习的任务，重点探讨在线弱分类器的不同之处，提出了一种新颖的在线增强算法，并通过理论分析设计了算法参数以及确定弱分类器个数等问题，实验结果表明所提出的算法比现有的在线算法效果更好。