中文摘要：

提出了一种对恒星光谱识别的新方法。根据恒星光谱数据的特性，我们以支持向量机为核心技术构建光谱识别器。由于恒星光谱数据通常含有较高的噪声，如果直接进行分类，识别率往往较低。因此作者首先采用小波分析的方法对原始光谱数据进行降噪预处理，提取光谱的特征，然后馈送到支持向量机完成对光谱数据的最终识别。利用实际光谱数据（Jacoby，1984）对所提出的技术进行检测，实验结果表明使用这种小波分析结合支持向量机的技术的识别效果要优于使用支持向量机结合主分量分析降维技术的识别方法。另外，作者还比较了支持向量机与传统甄别分析的分类性能，对实际及合成光谱进行实验的结果显示了支持向量机的识别正确率不但优于常见的5种甄别分析方法的识别率，而且有较强的泛化能力。

英文摘要：

The present paper describes a new technique for stellar spectral recognition. Considering the characteristics of stellar spectral data, support vector machine （SVM） was adopted to build a recognition system as kernel. Because stellar spectral data sets are usually extremely noisy, the correct classification rate of direct applying SVM is low. Consequently, wavelet de-noising method was proposed to reduce noise first and extract the main characteristics of stellar spectra. Then SVM was used for the recognition. Based on the real-world stellar spectra contributed by Jacoby et al. （1984）, it has proven that there will be a better performance using this composite classifier which combines wavelet and SVM than using SVM with principle component analysis data dimension reduction teehniqu. From the experiment of comparison of discriminant analysis and SVM based on stellar spectra for evolutionary synthesis, we can see that the correct classification rate of SVM is higher than that of discriminant analysis methods, and a well generalization ability is achieved.