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网络分类的优化改进
Boosted optimization for network classification |
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| 课程网址: |
http://videolectures.net/aistats2010_hancock_bofnc/
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| 主讲教师: |
Timothy Hancock
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| 开课单位: |
京都大学
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| 开课时间: |
2010-01-03
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| 课程语种: |
日语
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| 中文简介: |
本文提出了一种新的分类算法，该算法基于增强学习和消息传递之间的算法相似性，适用于复杂网络。我们将网络分类器视为逻辑回归，其中变量定义节点，交互作用定义边缘。根据这个定义，我们将问题表示为局部指数损失函数的因子图。利用因子图表示可以将网络分类器解释为单个节点分类器的集合。然后将增强学习的思想与网络优化算法相结合，定义了两种新的算法:增强期望传播(cep)和增强消息传递(BMP)。这些算法通过局部加权每个节点分类器来优化全局网络分类器的性能。我们比较了cep和BMP与逻辑回归的性能，以及在模拟网格结构网络上的惩罚逻辑回归模型的现状。结果表明，利用局部增强来优化网络分类器的性能可以提高分类性能，在必须考虑整个网络结构才能进行准确分类的情况下，这种方法尤其有效。
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| 课程简介: |
In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of local exponential loss functions. Using the factor graph representation it is possible to interpret the network classifier as an ensemble of individual node classifiers. We then combine ideas from boosted learning with network optimization algorithms to define two novel algorithms, Boosted Expectation Propagation (BEP) and Boosted Message Passing (BMP). These algorithms optimize the global network classifier performance by locally weighting each node classifier by the error of the surrounding network structure. We compare the performance of BEP and BMP to logistic regression as well state of the art penalized logistic regression models on simulated grid structured networks. The results show that using local boosting to optimize the performance of a network classifier increases classification performance and is especially powerful in cases when the whole network structure must be considered for accurate classification.
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| 关 键 词: |
网络分类; 优化改进; 逻辑回归
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-10-31：lxf
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| 阅读次数: |
43
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