Learn++: An incremental learning algorithm for supervised neural networks
Learn++: An incremental learning algorithm for supervised neural networks
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DOI：
10.1109/5326.983933
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发表时间：
2001-11-01
期刊：
影响因子：
--
通讯作者：
Honavar, V
中科院分区：
文献类型：
--
作者：
Polikar, R;Udpa, L;Honavar, V
我们介绍了Learn ++，这是一种用于神经网络（NN）模式分类器增量培训的算法。所提出的算法可以使受监督的NN范式（例如多层感知器（MLP））适应新数据，包括与以前看不见的类相对应的示例。此外，该算法在随后的增量学习会话中不需要访问先前使用的数据，但同时又不忘记先前获得的知识。 Learn ++使用根据精心量身定制的分布采样的培训数据来生成多个假设，利用分类器的集合。通过加权多数投票程序将结果分类器的输出组合在一起。我们在几个基准数据集以及现实世界的分类任务上介绍了仿真结果。初始结果表明，所提出的算法在实践中效果很好。还提供了由学习++构建的分类器误差的理论上界。
We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided.