In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is in

搭建OFDM通信系统的框架，用于信号特征提取、信号消噪，最终的权值矩阵就是滤波器的系数，时间序列数据分析中的梅林变换工具，包含特征值与特征向量的提取、训练样本以及最后的识别，迭代自组织数据分析。- Build a framework OFDM communication system, For feature extraction, signal de-noising, The final weight matrix is ??the filter coefficient, Time serie