Algorithms for clustering data
Algorithms for clustering data
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
An Analysis of Some Graph Theoretical Cluster Techniques
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Query by committee, linear separation and random walks
Theoretical Computer Science
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
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Using Active Learning in Intrusion Detection
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Semi-Supervised Active Learning Framework for Image Retrieval
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A unified framework for image retrieval using keyword and visual features
IEEE Transactions on Image Processing
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inconsistency-based active learning for support vector machines
Pattern Recognition
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Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.