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学习定向削减成本

The Cost of Learning Directed Cuts
课程网址: http://videolectures.net/mlg07_gaertner_tcoldc/  
主讲教师: Thomas Gartner
开课单位: 弗劳恩霍夫协会
开课时间: 2007-09-05
课程语种: 英语
中文简介:
在有向图中对顶点进行分类是一种重要的机器学习设置,具有许多应用。我们考虑在有三个特征属性的有向图上学习问题:(i)目标概念对应于有向切割; (ii)寻找裁员的总费用必须先验地界定; (iii)目标概念可能因隐藏的背景而改变。对于一个激励示例,考虑在某些过程中对中间产品进行分类,例如,用于制造汽车或软件中的控制流,如有缺陷或正确的那样。该过程可以用有向图表示,概念是单调的:出现在中间产品中的典型故障也将出现在产品的后期阶段。该概念可能取决于隐藏变量,因为一些预组装部件可能变化,并且故障可能仅针对某些费用而不是针对其他费用而发生。为了能够在产生故障的产品的成本和找到故障原因所需的成本之间进行权衡,需要严格的性能保证以找到错误。
课程简介: Classifying vertices in digraphs is an important machine learning setting with many applications. We consider learning problems on digraphs with three characteristic properties: (i) The target concept corresponds to a directed cut; (ii) the total cost of finding the cut has to be bounded a priori; and (iii) the target concept may change due to a hidden context. For one motivating example consider classifying intermediate products in some process, e.g., for manufacturing cars or the control flow in software, as faulty or correct. The process can be represented by a digraph and the concept is monotone: Typical faults that appear in an intermediate product will also be present in later stages of the product. The concept may depend on a hidden variable as some pre-assembled parts may vary and the fault may occur only for some charges and not for others. In order to be able to trade off between the cost of having a faulty product and the costs needed to find the cause of the fault, tight performance guarantees for finding the bug are needed.
关 键 词: 模式分析; 统计建模; 计算学习
课程来源: 视频讲座网
最后编审: 2020-06-01:吴雨秋(课程编辑志愿者)
阅读次数: 44