A morphologically optimal strategy for classifier combination: multiple expert fusion as a tomographic process

Windridge D.; Kittler J.

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A morphologically optimal strategy for classifier combination: multiple expert fusion as a tomographic process

机译：分类器组合的一种形态学最佳策略：将多专家融合作为层析过程

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摘要

We specify an analogy in which the various classifier combination methodologies are interpreted as the implicit reconstruction, by tomographic means, of the composite probability density function spanning the entirety of the pattern space, the process of feature selection in this scenario amounting to an extremely bandwidth-limited Radon transformation of the training data. This metaphor, once elaborated, immediately suggests techniques for improving the process, ultimately defining, in reconstructive terms, an optimal performance criterion for such combinatorial approaches.

机译：我们指定了一个类比，其中将各种分类器组合方法通过层析成像方法解释为跨越整个模式空间的复合概率密度函数的隐式重建，在这种情况下，特征选择的过程总计需要极高的带宽-训练数据的有限Radon变换。一旦详细阐述了这个比喻，便立即提出了改进流程的技术，最终以可重构的方式定义了这种组合方法的最佳性能标准。

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来源
《IEEE Transactions on Pattern Analysis and Machine Intelligence》 |2003年第3期|p.343-353|共11页
作者
Windridge D.; Kittler J.;
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作者单位

Dept. of Electron. & Electr. Eng., Surrey Univ., Guildford, UK;

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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
pattern recognition; Radon transforms; image classification; deconvolution; morphologically optimal strategy; classifier combination; multiple expert fusion; tomographic process; feature selection; Radon transformation; optimal performance;

机译：模式识别;on变换图像分类;去卷积形态最佳策略;分类器组合;多专家融合;断层扫描过程;特征选择;on转化;最佳表现;

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A morphologically optimal strategy for classifier combination: multiple expert fusion as a tomographic process

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