近似牛顿算法用于Ising模型推理,加快了收敛速度,实现了最优性能,避免了过拟合

U. Ferrari
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引用次数: 18

摘要

反问题包括推断模型分布的参数,这些参数能够适当地拟合实验数据集的选择特征。逆伊辛问题具体包括寻找再现二进制数据集的频率和相关性的最大熵分布。为了解决这一问题,我们提出了一种利用数据知识提供的对数似然函数周围的解决方案的算法。结果表明,该算法比标准梯度上升方法更快。此外,通过将算法收敛视为随机过程,我们适当地定义了过拟合,并展示了本算法如何通过构造来避免过拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximated Newton Algorithm for the Ising Model Inference Speeds Up Convergence, Performs Optimally and Avoids Over-fitting
Inverse problems consist in inferring parameters of model distributions that are able to fit properly chosen features of experimental data-sets. The Inverse Ising problem specifically consists of searching for the maximal entropy distribution reproducing frequencies and correlations of a binary data-set. In order to solve this task, we propose an algorithm that takes advantage of the provided by the data knowledge of the log-likelihood function around the solution. We show that the present algorithm is faster than standard gradient ascent methods. Moreover, by looking at the algorithm convergence as a stochastic process, we properly define over-fitting and we show how the present algorithm avoids it by construction.
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