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主动学习水平集复合函数
Actively Learning Level-Sets of Composite Functions |
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| 课程网址: |
http://videolectures.net/icml08_bryan_alls/
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| 主讲教师: |
Brent Bryan
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| 开课单位: |
卡内基梅隆大学
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| 开课时间: |
2008-07-28
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| 课程语种: |
英语
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| 中文简介: |
科学家经常有多种类型的实验和数据集，在这些实验和数据集上，他们可以测试参数化模型的有效性，并找到模型参数的合理区域。通过检查多个数据集，这些科学家可以获得他们的问题的推论，这些推论通常比从每个数据源独立得出的推论更有信息性。一些标准的数据组合技术产生一个目标函数，它是观测数据源的加权和。对模型参数空间的合理区域的计算约束可以表述为寻找目标函数的一个指定的水平集。针对这一问题，我们提出了一种主动学习算法，在每一步中都会选择一个参数设置（从参数空间）和一个实验类型来计算下一个样本。对八参数宇宙学模型的合成函数和真实数据的经验测试表明，我们的算法显著减少了识别所需区域所需的样本数量。
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| 课程简介: |
Scientists frequently have multiple types of experiments and data sets on which they can test the validity of their parametrized models and locate plausible regions for the model parameters. By examining multiple data sets, these scientists can obtain inferences for their problems which typically are much more informative than the deductions derived from each of the data sources independently. Several standard data combination techniques result in a target function which is a weighted sum of the observed data sources. Computing constraints on the plausible regions of the model parameter space can be formulated as that of finding a specified level set of the target function. We propose an active learning algorithm for this problem which at each step selects both a parameter setting (from the parameter space) and an experiment type upon which to compute the next sample. Empirical tests on synthetic functions and on real data for a eight parameter cosmological model show that our algorithm significantly reduces the number of samples required to identify desired regions.
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| 关 键 词: |
参数化模型; 目标函数; 主动学习算法
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-06：lxf
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| 阅读次数: |
38
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