Improving Logitboost with prior knowledge
Improving Logitboost with prior knowledge
复制标题
DOI：
10.1016/j.inffus.2011.11.004
复制
发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
T. Kanamori;Takashi Takenouchi
中科院分区：
文献类型：
--
作者：
T. Kanamori;Takashi Takenouchi
本研究的目的是将先验知识纳入提升算法。现有的方法需要额外的样本，代表先验知识。此外，为了调整训练样本中的信息与数据域中的先验知识之间的平衡，需要使用不同的正则化参数重复boosting算法。这些属性导致昂贵的计算。在本文中，我们提出了一个提升算法与先验知识，避免了计算问题。在我们的方法中，估计量和先验知识的混合分布被考虑。我们描述的数值实验表明我们的方法的有效性。
The purpose of this study is to incorporate prior knowledge into a boosting algorithm. Existing approaches require additional samples that represent the prior knowledge. Moreover, in order to adjust the balance between the information in training samples and the prior knowledge in the data domain, one needs to repeat the boosting algorithm with a different regularization parameter. These properties lead to costly computation. In this paper, we propose a boosting algorithm with prior knowledge that avoids computational issues. In our method, the mixture distribution of the estimator and prior knowledge is considered. We describe numerical experiments showing the effectiveness of our approach.