Feature Selection using Matlab. The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Se

A distributed PSOSVM hybrid system with feature selection and parameter optimization -Abstract This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the clas

针对目标与背景两类图像模式识别问题,在已有的特征选择方法基础上,提出了一种新颖的基于免疫分子编码机理的图像特征选择方法(IACA). 该方法借鉴生物免疫系统的抗体分 子编码机理,在对样本进行参数估计情况下,提出熵度量单个特征对于目标和背景的识别敏感度 从集合的角度研究并且定义了特征之间的包含和互补关系 并且基于组成抗体分子氨基酸结合能量最小原则,提出了关于图像目标的免疫抗体构建规则 最终实现了寻找最优特征子集的算法IACA ,该特征子集的维数通过算法自动获得无需人为设定,选择结果为目标的“免

Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature