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首页> 外文会议>International Conference on Computational Intelligence and Security(CIS 2005) pt.1; 20051215-19; Xi'an(CN) >Moderated Innovations in Self-poised Ensemble Learning
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Moderated Innovations in Self-poised Ensemble Learning

机译：自我平衡合奏学习的适度创新

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摘要

Self-poised ensemble learning is based on the idea of introducing an artificial innovation to the map to be predicted by each machine in the ensemble such that it compensates the error incurred by the previous one. We will show that this approach is equivalent to regularize the loss function used to train each machine with a penalty term which measures decorrelation with previous machines. Although the algorithm is competitive in practice, it is also observed that the innovations tend to generate an increasedly bad behavior of individual learners in time, damaging the ensemble performance. To avoid this, we propose to incorporate smoothing parameters which control the introduced level of innovation and can be characterized to avoid an explosive behavior of the algorithm. Our experimental results report the behavior of neural networks ensembles trained with the proposed algorithm in two real and well-known data sets.
机译：自我平衡的集成学习基于以下思想：将人工创新引入到要由集成中的每台机器预测的地图上，从而补偿前一个生成的错误。我们将证明，这种方法等效于对用于训练每台机器的损失函数进行正则化，该函数使用惩罚项来衡量与先前机器的去相关性。尽管该算法在实践中具有竞争力，但还可以观察到，这些创新往往会及时导致个别学习者的行为日益恶化，从而损害整体表现。为了避免这种情况，我们建议合并平滑参数，以控制引入的创新水平，并且可以对其进行特征化，以避免算法的爆炸性行为。我们的实验结果报告了在两个真实和众所周知的数据集中使用该算法训练的神经网络集合的行为。

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