First , the theories of the music algorithm and the esprit are presented here . conventional algorithms are limited by the array configuration , and a constructing vectors algorithm , which uses the correlative function of array data , is proposed in this paper . this algorithm is n ' t restricted within the special array configuration , and it is also very steady 在介绍了多重信号分类( music )算法和旋转不变技术( esprit )的基本原理后,考虑到常规的算法都受到阵列形式的限制,本文在esprit算法的基础上,提出了一种利用阵元数据的相关函数构造向量的算法,该算法不要求特定阵列结构,且有一定的稳健性。
In this paper , we begin with the analysis of wavelet transform . after the analysis of image wavelet coefficients and methods of image compression , a method of vector - constitution among different subbands , making verctor book using pcc + lbg , and fast vq is presented . at the same time a better compression performance is improved by using multistage vector algorithm , the design of this algorithm based on dsps is given at the end of this paper 该算法充分利用了小波分解后各子带间的相关性,跨子带构造高维数矢量,利用改进的渐进构造聚类( pcc )结合lbg的算法生成了具有代表性的最优码书,并提取特征矢量快速实现矢量量化,最后通过二级量化进一步降低矢量量化的复杂度。
Statistical learning theory derives necessary and sufficient conditions for consistency and fast rate of convergence of the empirical risk minimization principle , which is the basis of most traditional learning algorithms . it also theoretically underpins the support vector algorithms . support vector learning algorithm is based on structural risk minimization principle 传统的学习算法大多是基于经验风险最小化原则的,统计学习理论给出了经验风险最小化原则一致和快速收敛的充分和必要条件,并且为支持向量算法做了理论支持。