Characterization and detection of noise in clustering
Pattern Recognition Letters
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Active semi-supervised fuzzy clustering for image database categorization
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Geometrical fuzzy clustering algorithms
Fuzzy Sets and Systems
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Voronoi-Based segmentation of cells on image manifolds
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction
Artificial Intelligence in Medicine
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Classifying large datasets without any a priori information poses a problem especially in the field of bioinformatics. In this work, we explore the problem of classifying hundreds of thousands of cell assay images obtained by a high-throughput screening camera. The goal is to label a few selected examples by hand and to automatically label the rest of the images afterwards. Up to now, such images are classified by scripts and classification techniques that are designed to tackle a specific problem. We propose a new adaptive active clustering scheme, based on an initial fuzzy c-means clustering and learning vector quantization. This scheme can initially cluster large datasets unsupervised and then allows for adjustment of the classification by the user. Motivated by the concept of active learning, the learner tries to query the most ''useful'' examples in the learning process and therefore keeps the costs for supervision at a low level. A framework for the classification of cell assay images based on this technique is introduced. We compare our approach to other related techniques in this field based on several datasets.