Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
In order to reduce human efforts, there has been increasing interest in applying active learning for training text classifiers. This paper describes a straightforward active learni...
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, Jizhi W...
Deploying a classifier to large-scale systems such as the web requires careful feature design and performance evaluation. Evaluation is particularly challenging because these larg...