Batch Mode Active Learning for Networked Data
نویسندگان
چکیده
منابع مشابه
A Batch Mode Active Learning for Networked Data
We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to l...
متن کاملDiscriminative Batch Mode Active Learning
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning have focused on selecting one unlabeled instance to label at one time while retraining in each iteration. Recently a few batch mode active learning approaches have been proposed that select a set of most informative un...
متن کاملActive Learning for Networked Data
We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological network analysis and document classif...
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Active learning and transfer learning are two different methodologies that address the common problem of insufficient labels. Transfer learning addresses this problem by using the knowledge gained from a related and already labeled data source, whereas active learning focuses on selecting a small set of informative samples for manual annotation. Recently, there has been much interest in develop...
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Active Learning has been applied in many real world classification tasks to reduce the amount of labeled data required for training a classifier. However most of the existing active learning strategies select only a single sample for labeling by the oracle in every iteration. This results in retraining the classifier after each sample is added which is quite computationally expensive. Also many...
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2012
ISSN: 2157-6904,2157-6912
DOI: 10.1145/2089094.2089109