摘要翻译:
我们解决了从Maslov和Zhang提出的“知识网络”的噪声版本中检索信息的问题。我们把这个问题映射到一个无序的统计力学模型上,这为许多分析和数值方法打开了大门。我们给出了复制对称解,并与数值模拟进行了比较,最后讨论了它在美国参议院实际数据中的应用。
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英文标题:
《Retrieving information from a noisy "knowledge network"》
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作者:
Julien Barre
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最新提交年份:
2007
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分类信息:
一级分类:Physics 物理学
二级分类:Statistical Mechanics 统计力学
分类描述:Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变,热力学,场论,非平衡现象,重整化群和标度,可积模型,湍流
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一级分类:Physics 物理学
二级分类:Disordered Systems and Neural Networks 无序系统与神经网络
分类描述:Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜;随机、非周期和准周期系统的性质;无序介质中的传输;本地化;由缺陷和无序介导的现象;神经网络
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英文摘要:
We address the problem of retrieving information from a noisy version of the ``knowledge networks'' introduced by Maslov and Zhang. We map this problem onto a disordered statistical mechanics model, which opens the door to many analytical and numerical approaches. We give the replica symmetric solution, compare with numerical simulations, and finally discuss an application to real datas from the United States Senate.
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PDF链接:
https://arxiv.org/pdf/704.3983