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首页> 外文会议>12th European Conference on Machine Learning, 12th, Sep 5-7, 2001, Freiburg, Germany >Building Committees by Clustering Models Based on Pairwise Similarity Values
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Building Committees by Clustering Models Based on Pairwise Similarity Values

机译：基于成对相似值的聚类模型构建委员会

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摘要

Forming a committee is an approach for integrating several opinions or functions instead of favouring a single one. Selecting and weighting the committee members is done in several ways by different algorithms. Possible solutions to this problem is still the topic of current research. Our starting point is the decomposition of the committee error into a bias- and variance-like term. Two requests can be derived from this equation: Models should on the one hand be regularized properly to reduce the average error. On the other hand they should be as independent as possible (in the mathematical sense) to decrease the committee error. The first request of regularization can be handled by a Bayesian learning framework. For the second request T want to suggest a new selection method for committee members based on the pairwise stochastical dependence of their output functions, which maximizes the overall independence. Given these pairwise similarity values the models can be separated in classes by a hierarchical clustering algorithm. From the committee error decomposition I derive a criterion that allows to find the optimal number of classes, i.e. the optimal stop criteria for the clustering algorithm. The benefits of the approach are demonstrated on a noisy benchmark problems as well as on the prediction of newspaper sales rates for a large number of retail traders.
机译：成立委员会是一种整合多种意见或职能而不是赞成一种意见或职能的方法。通过不同的算法以多种方式选择和加权委员会成员。该问题的可能解决方案仍然是当前研究的主题。我们的出发点是将委员会错误分解成一个类似偏差和方差的术语。从该方程式可以得出两个要求：一方面，应适当地对模型进行正则化以减少平均误差。另一方面，它们应尽可能独立（从数学意义上讲），以减少委员会错误。可以通过贝叶斯学习框架处理对正则化的第一个请求。对于第二个请求，T希望根据委员会成员的输出功能的成对随机依赖性提出一种新的委员会成员选择方法，该方法最大程度地提高了整体独立性。给定这些成对的相似性值，可以通过分层聚类算法将模型分为几类。从委员会错误分解中，我得出了一个准则，该准则允许找到最佳的类数，即聚类算法的最佳停止准则。这种方法的好处在嘈杂的基准问题以及对大量零售贸易商的报纸销售率预测中得到了证明。

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