A Novel Learning Classification on Pattern Recognition

机译：一种新颖的模式识别学习分类

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In this paper we propose a novel approach to model pattern classification based on tangent distance within a statistical framework for classification. Statistical characteristics of classifier data are analyzed by the parametric estimates in the regression equation. We research on feature extraction for the training data in statistical pattern recognition by transformation and optimal Bayesian decision rule. Finally, we give our experiments on automatic speech recognition. The experimental results show the effectiveness of our approach.

机译：在本文中，我们提出了一种在分类统计框架内基于切线距离的模式分类的新方法。分类器数据的统计特征通过回归方程式中的参数估计值进行分析。我们研究了通过变换和最佳贝叶斯决策规则对统计模式识别中的训练数据进行特征提取。最后，我们给出了自动语音识别的实验。实验结果表明了该方法的有效性。

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《International conference on electronic engineering, communication and management;EECM 2011》|2011年|p.273-279|共7页
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作者
Yu-jie Sang;
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原文格式 PDF
正文语种
中图分类通信;
关键词
optimal bayesian decision; parametric estimates; pattern classification; pattern recognition;

机译：最佳贝叶斯决策;参数估计;模式分类模式识别;

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9. Learning probability spaces for classification and recognition of patterns with or without supervision [R] . Hancock, J. C., Patrick, E. A. 1965

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A Novel Learning Classification on Pattern Recognition

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