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学习使用缺失和损坏的功能进行分类

Learning to Classify with Missing and Corrupted Features
课程网址: http://videolectures.net/icml08_shamir_lcm/  
主讲教师: Ohad Shamir
开课单位: 魏茨曼科学研究所
开课时间: 2008-08-07
课程语种: 英语
中文简介:
在使用机器学习算法训练分类器并在现实世界系统中使用之后,它经常面对未出现在训练数据中的噪声。特别是,某些功能子集可能丢失或可能已损坏。我们提出了两种新颖的机器学习技术,这种技术对这种分类时间噪声具有鲁棒性。首先,我们使用线性编程来解决学习问题的近似。我们分析了近似的紧密性,并证明了这种方法的统计风险界限。其次,我们定义了我们问题的在线学习变体,使用修改过的Perceptron来解决这个变体,并使用在线到批处理技术获得统计学习算法。我们总结了一组实验,证明了我们的算法的有效性。
课程简介: After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularly, some subset of features may be missing or may become corrupted. We present two novel machine learning techniques that are robust to this type of classification-time noise. First, we solve an approximation to the learning problem using linear programming. We analyze the tightness of our approximation and prove statistical risk bounds for this approach. Second, we define the online-learning variant of our problem, address this variant using a modified Perceptron, and obtain a statistical learning algorithm using an online-to-batch technique. We conclude with a set of experiments that demonstrate the effectiveness of our algorithms.
关 键 词: 机器学习算法; 分类器; 线性编程
课程来源: 视频讲座网
最后编审: 2019-04-21:lxf
阅读次数: 63