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Image_Feature_Selection_Method_Based_on_Immune_Enc
- 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
information-theory
- Matlab implementation of various entropy in information theory, including the calculation of self information,信息论中各种熵的matlab实现，其中包括自信息量，互信息量，条件熵，联合熵，冗余度等等的计算
information
- Matlab implementation of various entropy in information theory, including the calculation of self information,信息论中各种熵的matlab实现
MULT-information
- Matlab implementation of various entropy in information theory, including the calculation of self information
information-calculation
- Matlab implementation of various entropy in information theory, including the calculation of self information,信息论中各种熵的matlab实现，其中包括自信息量，互信息量，条件熵，联合熵
calculation-of-self-information
- 属性相关性Matlab implementation of various entropy in information theory, including the calculation of self information,信息论中各种熵的matlab实现
1.self-information
- 属性相关性Matlab implementation of various entropy in information theory, including the calculation of self information,信息论中各种熵的matlab实现