一种基于SVM的多层分类策略

提出了一种新的基于反例文档选择的多层分类策略I-vs-brothers。与原策略相比，该策略在训练阶段仅仅选择兄弟节点包含的样例文档作为反例，从而减少了较深层次节点需要学习的文档。实验结果表明，在该文的实验条件下，基于该策略的算法使得训练效率提高了60％，而分类精度却基本上保持不变。该策略还可以用在I-vs-I之上形成I-vs-brother策略，用来减少多层分类情况下节点训练时需要对比学习的节点数目。

A Strategy of Multi-level Classification Based on SVM

This paper proposes a new multi-level classification strategy named 1-vs-brothers on the basis of 1-vs-rest strategy which is used to transfer multi-category problems to two category problem. Compared with the original strategy, the new strategy which is based on the selection of negative examples, only selects the example documents of brother nodes as negative examples, that cuts down the documents number needed to learn during the non-first level nodes training period. The experiment shows that on the data of this paper the algorithm based on this strategy improves the training efficiency about 60 percent, and the classification precision yet remains no change on the whole. This strategy can also be used on 1-vs-1 strategy to form 1-vs-brother strategy, which will cut down the node numbers needed to learning during the training period of multi-level classification.