Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
As an effect tool of pattern recognition and data processing, rough set theory (RST) and support vector machine (SVM) have become the focus of research in machine learning.
粗糙集理论(rst)与支持向量机(SVM)作为模式识别，数据处理的有效工具，已成为机器学习的研究热点。
As new technology of data mining, support vector machines (SVM) have been successfully applied in pattern recognition and regression problem, et al.
支持向量机作为数据挖掘的一项新技术，应用于模式识别和处理回归问题等诸多领域。
Vector Quantization (VQ) is one of popular data compression and data coding methods for speech recognition at present.
矢量量化(VQ)是语音识别中广泛应用的一种数据压缩和编码方法。
Using the subimage of an unknown target as feature vector and minimum distance rule for target recognition, experiments on simulated data are done.
对未知目标，以其子像对库矢量的欧氏距离最小为分类准则，进行了识别模拟实验。
To improve the performance of speaker recognition in the condition of noise and little speech data, feature parameters were studied based on the Vector Quantization (VQ).
为了使说话人识别系统在语音较短和存在噪声的环境下也具有较高的识别率，基于矢量量化识别算法，对提取的特征参数进行研究。
To improve the performance of speaker recognition in the condition of noise and little speech data, feature parameters were studied based on the Vector Quantization (VQ).
为了使说话人识别系统在语音较短和存在噪声的环境下也具有较高的识别率，基于矢量量化识别算法，对提取的特征参数进行研究。
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