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English(EN) A Data-Driven Approach to State Construction in Markov Models

新框架使用无监督学习进行马尔可夫模型状态构建

本文介绍了一种使用数据驱动方法构建马尔可夫模型状态的新框架。它通过结合监督特征选择和无监督学习技术(如基于密度的聚类、谱聚类和Kohonen自组织映射)来解决任意状态定义的常见问题。研究表明,谱聚类和Kohonen自组织映射在识别固有结构方面特别有效,从而提高了马尔可夫模型的准确性和预测能力。 AI

影响 为改进应用马尔可夫建模中的状态定义提供了一个方法论框架,有可能提高这些模型的准确性和预测能力。

排序理由 该集群包含一篇学术论文,详细介绍了马尔可夫模型状态构建的新方法论框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新框架使用无监督学习进行马尔可夫模型状态构建

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该集群包含一篇学术论文,详细介绍了马尔可夫模型状态构建的新方法论框架。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Linde Van Gestel, Marie-Anne Guerry, Evy Rombaut ·

    马尔可夫模型中状态构建的数据驱动方法

    arXiv:2608.21480v1 Announce Type: new Abstract: A Markov chain is a widely used stochastic process modelling random events over time. These models are built on subsets of the entire dataset, referred to as states, which are considered to be homogeneous regarding transition probab…