vs_build_tools.zip
visual studio 2015-2019 build tools,2017和2019还包含了不同的版本
visual studio 2015-2019 build tools,2017和2019还包含了不同的版本
集装箱箱号图像及标注,共1051张图像。标注方式为整个箱号标注,例如CSLU8116467,标注结果为CSLU8116467,非单个字符标注。
由邓百川等提出的一种近红外光谱特征提取算法,A bootstrapping soft shrinkage (BOSS) approach for variable selection
光谱信息的特征选择,通过云永欢等提出的VCPA来进行光谱信息的特征选择(文件中包含了VCPA,IRIV,VCPA-GA以及VCPA-IRIV等光谱的变量选择算法)。In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA–iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP–GA) were used to investigate the improvement of VCPA-based hybrid strategy.
特征选择是常用的预处理任务之一,其目的是减少智能算法和模型的输入量。这有助于简化模型,降低模型训练的计算成本,提高模型的泛化能力和防止过度训练。用于前馈人工神经网络(ANNs)训练的进化特征选择的MATLAB实现。
基于自标度数据的偏最小二乘(PLS)回归系数是一个重要变量的理论,云永欢等提出了一种新的变量选择策略&迭代变量子集优化(IVSO)。在这项工作中,每个子模型中产生的回归系数都被规范化以消除影响。在每一轮迭代中,将从子模型中得到的各变量的回归系数相加,以评估其重要性水平。采用加权二元矩阵抽样(WBMS)和序贯加法两步法,以竞争的方式逐步、温和地消除非信息变量,降低重要变量丢失的风险。此外,还考虑到,通过交叉验证产生的潜在变量的最佳数量将对回归系数产生很大的差异,有时这种差异甚至可以变化几个数量级。