核覆盖算法在光谱分类问题中的研究

针对光谱分类，提出了一种基于核技巧的覆盖算法——核覆盖算法。该算法将核技巧与覆盖算法相结合，并在特征空间中抽取支持向量。实验表明核覆盖算法在光谱分类中的精度与SVM相差不大，但是它只涉及距离的计算，不必象SVM那样求解二次规划问题，对于核宽的选择也不象SVM那样非常敏感。核覆盖算法与覆盖算法相比分类性能相当，它的优势在于引入的非线性映射Φ改变了样本集在特征空间中之间的距离关系，使得核覆盖算法得到的支持向量个数大大少于覆盖算法。

Studies of Spectra Classification Based on Kernel Covering Algorithm

A kernel based covering algorithm,called the kernel covering algorithm(KCA), is proposed for the classification of celestial spectra.This algorithm is a combination of kernel trick with the covering algorithm,and is used to extract the support vectors in feature space.The experiments show that the classification result based on KCA is a little less than that based on SVM.However,KCA only involves the distance computation without the need to solve the quadratic programming problem.Also,KCA is insensitive to the width of gauss window.Although KCA has a comparable classification performance with the covering algorithm,it changes the distance between samples in feature space by the nonlinear mapping such that the distribution of samples is more adaptable to classify.Therefore,the number of KCA's resulting support vectors is significantly smaller than that of the covering algorithm.