基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。
Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed.
同时新算法结合了原VM8和基于零系数百分比算法的优点，可以获得更加精确的控制效果。
The new algorithm adopts some advantages of VM8 and Zero coefficient percentage algorithms. It can control the bits more accurately.
本文提出了一个基于模式分解树，不需要扫描原数据库的增量挖掘算法。
The paper presents an algorithm for incremental mining which is based on pattern decomposing tree and has no need for scanning old data base.
应用推荐