A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active learning with statistical models
Journal of Artificial Intelligence Research
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Repairing self-confident active-transductive learners using systematic exploration
Pattern Recognition Letters
ECML '07 Proceedings of the 18th European conference on Machine Learning
Directly Identify Unexpected Instances in the Test Set by Entropy Maximization
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Learning to segment from a few well-selected training images
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SED: supervised experimental design and its application to text classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning from only positive and unlabeled data using entropy
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Unbiased online active learning in data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning for node classification in assortative and disassortative networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning with semi-automatic annotation for extractive speech summarization
ACM Transactions on Speech and Language Processing (TSLP)
Active hashing and its application to image and text retrieval
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
Active learning for protein function prediction in protein-protein interaction networks
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Active learning with multi-label SVM classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Active learning from relative queries
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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An "active learning system" will sequentially decide which unlabeled instance to label, with the goal of efficiently gathering the information necessary to produce a good classifier. Some such systems greedily select the next instance based only on properties of that instance and the few currently labeled points -- e.g., selecting the one closest to the current classification boundary. Unfortunately, these approaches ignore the valuable information contained in the other unlabeled instances, which can help identify a good classifier much faster. For the previous approaches that do exploit this unlabeled data, this information is mostly used in a conservative way. One common property of the approaches in the literature is that the active learner sticks to one single query selection criterion in the whole process. We propose a system, MM+M, that selects the query instance that is able to provide the maximum conditional mutual information about the labels of the unlabeled instances, given the labeled data, in an optimistic way. This approach implicitly exploits the discriminative partition information contained in the unlabeled data. Instead of using one selection criterion, MM+M also employs a simple on-line method that changes its selection rule when it encounters an "unexpected label". Our empirical results demonstrate that this new approach works effectively.