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A divide-and-conquer method for multi-net classifiers

机译：多网分类器的分治法

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摘要

Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the 'divide-and-conquer' framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subt-asks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).
机译：几位研究人员表明，通过组合多个神经网络的输出，可以在棘手的模式识别问题上取得重大改进。在这项工作中，我们提出并测试了基于监督学习和无监督学习的模式分类多网络系统。遵循“分而治之”的框架，将输入空间划分为重叠的子空间，然后使用神经网络来求解各自的分类子需求。最后，将各个分类器的输出进行适当组合以获得最终分类决策。两种聚类方法已经应用于输入空间划分，并且考虑了两种方案来组合多个分类器的输出。对知名数据集的实验表明，与单网络训练相比，多网络分类系统在错误率和训练速度方面都表现出令人鼓舞的性能（尤其是如果分类器的训练是在平行）。

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