机译:一种具有稀疏性和多样性的分类器集成方法
Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China;
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
China National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;
Classifier ensemble; Sparsity learning; Diversity learning; Neural network ensembles; Genetic algorithm;
机译:TargetATPsite:具有残基演化图像稀疏表示和分类器集成的无模板ATP结合位点预测方法
机译:在具有不同多样性水平的合奏中使用加权动态分类器选择方法
机译:集成分类器多样性度量以减少基于特征选择的分类器集成
机译:使用具有稀疏性和多样性的启发式学习对分类器进行集成
机译:稀疏数据的稀疏模型:方法,局限性,可视化和集成
机译:基于加权精度和多样性测度的更好的分类器集成
机译:一种基于数据分集的无分类器集成选择方法 随机子空间