RandomBoost：通过随机化简化的多类增强
RandomBoost: Simplified Multi-class Boosting through Randomization
摘要：
我们提出了一种新的提升方法来解决多类分类问题，其中多个类本质上是由一组随机投影矩阵来区分的。该方法使用随机投影来减轻通常需要执行多类分类的二进制分类器的扩散。其结果是一个多类分类器与一个单一的向量值的参数，无论涉及的类的数量。提出了这种方法的两个变体。第一种方法将原始数据随机投影到新的空间中，而第二种方法将学习的弱分类器的输出随机投影。这些方法不仅概念简单，而且有效，易于实现。一系列的合成，机器学习和视觉识别数据集上的实验表明，我们提出的方法相比，现有的多类提升算法的收敛速度和分类精度。
英文摘要：
We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multi-class classification. The result is a multi-class classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning and visual recognition data sets demonstrate that our proposed methods compare favorably to existing multi-class boosting algorithms in terms of both the convergence rate and classification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
