Estimating the Bayes Point Using Linear Knapsack Problems
Estimating the Bayes Point Using Linear Knapsack Problems
复制标题
使用线性背包问题估计贝叶斯点
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
B. Potetz
中科院分区：
文献类型：
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作者：
B. Potetz
贝叶斯点机是通过估计分类器参数的后验分布的平均值来近似贝叶斯最优分类器的二元分类器。过去的贝叶斯点机器已经克服了这个目标的棘手性，通过使用消息传递技术，近似后验的分类器参数作为高斯分布。在本文中，我们研究替代的消息传递方法，不依赖于高斯近似。为了使这成为可能，我们引入了一个新的计算捷径的基础上线性多选择背包问题，降低了近似贝叶斯点置信传播消息的复杂性，从指数到线性的数据特征的数量。我们的方法的实证测试表明，在几个现实世界的UCI数据集的软利润支持向量机和期望传播贝叶斯点机器的线性分类显着改善。
A Bayes Point machine is a binary classifier that approximates the Bayes-optimal classifier by estimating the mean of the posterior distribution of classifier parameters. Past Bayes Point machines have overcome the intractability of this goal by using message passing techniques that approximate the posterior of the classifier parameters as a Gaussian distribution. In this paper, we investigate alternative message passing approaches that do not rely on Gaussian approximation. To make this possible, we introduce a new computational shortcut based on linear multiple-choice knapsack problems that reduces the complexity of approximating Bayes Point belief propagation messages from exponential to linear in the number of data features. Empirical tests of our approach show significant improvement in linear classification over both soft-margin SVMs and Expectation Propagation Bayes Point machines for several real-world UCI datasets.