摘要翻译:
本文的主要目的是为一类广泛的信度网络提出平均场近似,其中sigmoid网络和噪声OR网络是特例。这些近似是基于Plefka提出的强大的平均场理论。通过变分推导,我们证明了Saul,Jaakkola和Jordan的方法是Plefka方法中的一阶近似。Plefka理论在信度网络中的应用在计算上是不容易的。为了解决这个问题,我们提出了基于泰勒级数的新的近似方法。小尺度实验表明,所提出的方案是有吸引力的。
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英文标题:
《Mean Field Methods for a Special Class of Belief Networks》
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作者:
C. Bhattacharyya, S. S. Keerthi
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最新提交年份:
2011
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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 chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiments show that the proposed schemes are attractive.
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PDF链接:
https://arxiv.org/pdf/1106.0246