摘要翻译：
本文提出了一种精确计算线性链条件随机场梯度的数值稳定的递推算法。它是一种在对数域期望半环上的前向算法，当应用于长观测序列时，其目的是提高存储效率。与传统的基于前向-后向递归的算法不同，该算法的内存复杂度不依赖于序列长度。在实际数据上的实验表明，该方法对处理长序列的问题是有效的。
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英文标题：
《Gradient Computation In Linear-Chain Conditional Random Fields Using The
  Entropy Message Passing Algorithm》
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作者：
Velimir M. Ilic, Dejan I. Mancev, Branimir T. Todorovic, Miomir S.
  Stankovic
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The paper proposes a numerically stable recursive algorithm for the exact computation of the linear-chain conditional random field gradient. It operates as a forward algorithm over the log-domain expectation semiring and has the purpose of enhancing memory efficiency when applied to long observation sequences. Unlike the traditional algorithm based on the forward-backward recursions, the memory complexity of our algorithm does not depend on the sequence length. The experiments on real data show that it can be useful for the problems which deal with long sequences.
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PDF链接：
https://arxiv.org/pdf/1011.1478