Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the

算法百科，数据挖掘多种算法的集合，可以分类查询各种算法-Wikipedia algorithms, data mining, a collection of multiple algorithms, you can access a variety of classification algorithms

wikipedia机器学习迷你书：《Computational learning theory》（计算学习理论） 比较理论，汇集了统计学习理论，VC理论，PAC学习，稳定性，在线学习与分布式学习等基本概念。顺各条参考文献可更深入学习。-wikipedia machine learning mini book: Computational learning theory (calculated learning theory) comparison theory, a collection o

wikipedia跨模态（文本图像）数据集，可用于图像标注、跨媒体检索等领域。共十类，已标注。文本特征是10维LDA特征，图像特征是128维SIFT特征(Wikipedia datasets for cross-media retrieval, image annotation etc.)