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有关多分类器系统的三个小时

Three Hours on Multiple Classififier Systems
课程网址: http://videolectures.net/aop09_roli_thomc/  
主讲教师: Fabio Roli
开课单位: 卡里亚里大学
开课时间: 2009-12-03
课程语种: 英语
中文简介:
多分类器系统的动机和基本概念动机。“最坏”案例和“最佳”案例动机。实际和理论动机。基本概念。多分类器系统的体系结构。合奏类型,组合器类型。分类器的概念“多样性”。多分类器系统的设计周期。创建多个分类器用于创建分类器集合的系统方法。基于训练数据操作的方法:数据分割法、袋装法和增压法。基于输入输出特征操作的方法:特征选择、随机子空间方法、噪声注入和纠错码。组合多个分类器的方法在“抽象”级别组合多个分类器(投票方法、行为知识空间方法等)在“等级”级别组合多个分类器的方法(Borda计数方法等)在“测量”级别组合多个分类器的方法层次(线性组合器、产品规则等)的基本概念对动态分类器的选择方法。
课程简介: Motivations and basic concepts Motivations of multiple classifier systems. The “worst” case and “best” case motivations. Practical and theoretical motivations. Basic concepts. Architectures for multiple classifier systems. Ensemble types, combiner types. The concept of classifier “diversity”. The design cycle of a multiple classifier system. Creating multiple classifiers Systematic methods for creating classifier ensembles. Methods based on training data manipulation: data splitting methods, Bagging and Boosting. Methods based on input and output feature manipulation: feature selection, the Random Subspace method, noise injection, and error-correcting codes. Combining multiple classifiers Methods for combining multiple classifiers at the “abstract” level (voting methods, the Behaviour Knowledge Space method, etc.) Methods for combining multiple classifiers at the “rank” level (the Borda count method, etc.) Methods for combining multiple classifiers at the “measurement” level (linear combiners, the product rule, etc.) Basic concepts on dynamic classifier selection methods.
关 键 词: 多分类器; 机器学习; 分类器系统
课程来源: 视频讲座网
最后编审: 2019-12-19:lxf
阅读次数: 54