摘要翻译:
提出了一种基于多层感知器和粗糙集的神经粗糙模型。然后对神经粗糙模型进行了测试,从人口数据中模拟艾滋病毒的风险。该模型采用贝叶斯框架建立,并采用蒙特卡罗方法和Metropolis准则进行训练。当该模型被测试来估计艾滋病毒感染的风险时,根据人口统计数据,它的准确率为62%。该模型能够结合贝叶斯MLP模型的精确性和贝叶斯粗糙集模型的透明性。
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英文标题:
《Bayesian Approach to Neuro-Rough Models》
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作者:
Tshilidzi Marwala and Bodie Crossingham
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最新提交年份:
2007
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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:
This paper proposes a neuro-rough model based on multi-layered perceptron and rough set. The neuro-rough model is then tested on modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62%. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.
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PDF链接:
https://arxiv.org/pdf/0705.0761