贝叶斯多实例学习：自动特征选择和归纳传递][Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer]

贝叶斯多实例学习：自动特征选择和归纳传递 Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer


课程网址:	http://videolectures.net/icml08_raykar_bmi/
主讲教师:	Vikas Raykar
开课单位:	马里兰大学
开课时间:	2008-08-07
课程语种:	英语
中文简介:	我们提出了一种新颖的贝叶斯多实例学习算法。该算法自动识别相关的特征子集，并在学习多个（概念上相关的）分类器时利用归纳传递。实验结果表明，所提出的基线MIL方法比以前的MIL算法更精确，并且选择了一组更小的有用特征。与单独学习每个任务相比，归纳转移进一步提高了分类器的准确性。
课程简介:	We propose a novel Bayesian multiple instance learning algorithm. This algorithm automatically identifies the relevant feature subset, and utilizes inductive transfer when learning multiple (conceptually related) classifiers. Experimental results indicate that the proposed baseline MIL method is more accurate than previous MIL algorithms and selects a much smaller set of useful features. Inductive transfer further improves the accuracy of the classifier as compared to learning each task individually.
关键词:	贝叶斯; 分类器; 归纳传递
课程来源:	视频讲座网
最后编审:	2019-04-19：lxf
阅读次数:	68

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