In boosting, we aim to leverage multiple weak learners to produce a strong
learner. At the center of this paradigm lies the concept of building the strong
learner as a voting classifier, which outputs a weighted majority vote of the
weak learners. While many successful boosting algorithms, such as the iconic
AdaBoost, produce voting classifiers, their theoretical performance has long
remained sub-optimal: the best known bounds on the number of training examples
necessary for a voting classifier to obtain a given accuracy has so far always
contained at least two logarithmic factors above what is known to be achievable
by general weak-to-strong learners. In this work, we break this barrier by
proposing a randomized boosting algorithm that outputs voting classifiers whose
generalization error contains a single logarithmic dependency on the sample
size. We obtain this result by building a general framework that extends sample
compression methods to support randomized learning algorithms based on
sub-sampling.

通过提出一种随机增强算法，我们构建了一个通用框架，将样本压缩方法扩展到支持基于子采样的随机学习算法，并输出具有单对数依赖性的投票分类器的泛化误差，相比于已知可实现的一般的弱到强学习器，我们的算法在训练样本数量方面具有更好的表现。

提升、投票分类器与随机样本压缩方案

Boosting, Voting Classifiers and Randomized Sample Compression Schemes

Determination of mispronunciations and ensuring feedback to users are
maintained by computer-assisted language learning (CALL) systems. In this work,
we introduce an ensemble model that defines the mispronunciation of Arabic
phonemes and assists learning of Arabic, effectively. To the best of our
knowledge, this is the very first attempt to determine the mispronunciations of
Arabic phonemes employing ensemble learning techniques and conventional machine
learning models, comprehensively. In order to observe the effect of feature
extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel
spectrogram are blended with each learning algorithm. To show the success of
proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are
voiced by a total of 11 different person. The amount of data set has been
enhanced employing the methods of adding noise, time shifting, time stretching,
pitch shifting. Extensive experiment results demonstrate that the utilization
of voting classifier as an ensemble algorithm with Mel spectrogram feature
extraction technique exhibits remarkable classification result with 95.9% of
accuracy.

本文介绍了一种利用集成学习模型识别阿拉伯语音素发音错误并帮助学习阿拉伯语的方法，并使用 Mel 频谱图特征提取技术将投票分类器作为集成算法，取得了 95.9% 的分类准确率。