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多分类器系统的串行结构
On serial architectures for multiple classifier systems |
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| 课程网址: |
http://videolectures.net/mlws04_josef_samcs/
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| 主讲教师: |
Josef Kittler
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| 开课单位: |
萨里大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
多分类器融合是近年来出现的机器学习模式之一。文献中提出了大量构造多分类器系统的方法。其中大部分采用并行结构，包括通过某种形式的线性或非线性组合规则融合多个分类器。直观地说，我们可以将并行融合看作是一种尝试，通过组合对一个类的撇号概率的几个独立估计来提高性能，从而减少组合估计的方差。对于两个相互竞争的假设之间给定的概率裕度，这种减少的方差会导致在贝叶斯误差之上产生额外分类误差的概率较低。对于多分类器系统方案，通过控制竞争假设之间的裕度来提高性能的研究较少。通常，可以通过类分组来增加页边距。这种方法常常导致串行多分类器系统结构。根据分组结构是固定的还是动态创建的，得到的多分类器要么是决策树，要么是链状的多级系统。本文将对支持这种MCS方法的理论进行综述，并讨论其含义。结果表明，该理论导致了不同的类别分组/保证金操纵策略。将讨论它们的相对优势。其中一些策略的有效性将通过一个实际的目标识别问题来说明。
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| 课程简介: |
One of the recently emerged paradigms in machine learning is multiple classifier fusion. A large number of methods for constructing multiple classifier systems (MCS) have been suggested in the literature. The majority of these draw on a parallel architecture, involving a fusion of multiple classifiers via some form of linear or nonlinear combination rule. Intuitively, one can look at parallel fusion as an attempt to improve the performance by combining several independent estimates of a class aposteriori probability and thereby reducing the variance of the combined estimate. For a given probability margin between two competing hypotheses, this reduced variance then results in a lower probability of incurring an additional classification error over and above the Bayes' error. Much less attention has been paid to multiple classifier system schemes that aim to enhance the performance by manipulating the margin between competing hypotheses. An increased margin can normally be achieved by class grouping. This approach often leads to serial multiple classifier system architectures. Depending on whether the grouping structure is fixed or created dynamically, the resulting multiple classifier is either a decision tree or a chain like multistage system. In this paper the theory underpinning this MCS approach will be overviewed and its implications discussed. It will be shown that the theory leads to diverse class grouping/margin manipulation strategies. Their relative advantages will be discussed. The effectiveness of some of these strategies will be illustrated on a practical problem of object recognition.
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| 关 键 词: |
计算机科学; 机器学习; 贝叶斯
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-05-22：吴雨秋(课程编辑志愿者)
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| 阅读次数: |
50
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