Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth 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
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active learning: theory and applications
Active learning: theory and applications
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
On linear separability of data sets in feature space
Neurocomputing
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Application of distributed SVM architectures in classifying forest data cover types
Computers and Electronics in Agriculture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pool-based active learning in approximate linear regression
Machine Learning
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple-view multiple-learner active learning
Pattern Recognition
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel multi-view learning developed from single-view patterns
Pattern Recognition
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
Active learning with adaptive regularization
Pattern Recognition
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
Granular support vector machine based on mixed measure
Neurocomputing
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In classification tasks, active learning is often used to select out a set of informative examples from a big unlabeled dataset. The objective is to learn a classification pattern that can accurately predict labels of new examples by using the selection result which is expected to contain as few examples as possible. The selection of informative examples also reduces the manual effort for labeling, data complexity, and data redundancy, thus improves learning efficiency. In this paper, a new active learning strategy with pool-based settings, called inconsistency-based active learning, is proposed. This strategy is built up under the guidance of two classical works: (1) the learning philosophy of query-by-committee (QBC) algorithm; and (2) the structure of the traditional concept learning model: from-general-to-specific (GS) ordering. By constructing two extreme hypotheses of the current version space, the strategy evaluates unlabeled examples by a new sample selection criterion as inconsistency value, and the whole learning process could be implemented without any additional knowledge. Besides, since active learning is favorably applied to support vector machine (SVM) and its related applications, the strategy is further restricted to a specific algorithm called inconsistency-based active learning for SVM (I-ALSVM). By building up a GS structure, the sample selection process in our strategy is formed by searching through the initial version space. We compare the proposed I-ALSVM with several other pool-based methods for SVM on selected datasets. The experimental result shows that, in terms of generalization capability, our model exhibits good feasibility and competitiveness.