This work provides simple algorithms for multi-class (and multi-label)
prediction in settings where both the number of examples n and the data
dimension d are relatively large. These robust and parameter free algorithms
are essentially iterative least-squares updates and very versatile both in
theory and in practice. On the theoretical front, we present several variants
with convergence guarantees. Owing to their effective use of second-order
structure, these algorithms are substantially better than first-order methods
in many practical scenarios. On the empirical side, we present a scalable
stagewise variant of our approach, which achieves dramatic computational
speedups over popular optimization packages such as Liblinear and Vowpal Wabbit
on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art
accuracies.

该研究提供了一种用于大规模多类（和多标签）预测的简单算法，其鲁棒性强且不需要参数；这些基于二次结构的算法能够在理论和实践中胜过基于一阶方法的算法，并且通过可扩展的分阶段方法，通过增加计算速度改善准确率。