Algorithms for clustering data
Algorithms for clustering data
Information-based objective functions for active data selection
Neural Computation
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Learning in the presence of concept drift and hidden contexts
Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Incremental Induction of Decision Trees
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Active learning with multiple views
Active learning with multiple views
Aggregate features and ADABOOST for music classification
Machine Learning
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
A comparison of features for speech, music discrimination
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Data-driven decomposition for multi-class classification
Pattern Recognition
Extensions of vector quantization for incremental clustering
Pattern Recognition
Evolving Vector Quantization for Classification of On-Line Data Streams
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
IEEE Transactions on Knowledge and Data Engineering
Active learning with statistical models
Journal of Artificial Intelligence Research
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Increasing On-line Classification Performance Using Incremental Classifier Fusion
ICAIS '09 Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
Impact of object extraction methods on classification performance in surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Can adaboost.m1 learn incrementally? a comparison to learn++ under different combination rules
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual Interactive Systems for End-User Development: A Model-Based Design Methodology
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
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
Semi-supervised learning combining co-training with active learning
Expert Systems with Applications: An International Journal
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Active learning is understood as any form of learning in which the learning algorithm has some control over the input samples due to a specific sample selection process based on which it builds up the model. In this paper, we propose a novel active learning strategy for data-driven classifiers, which is based on unsupervised criterion during off-line training phase, followed by a supervised certainty-based criterion during incremental on-line training. In this sense, we call the new strategy hybrid active learning. Sample selection in the first phase is conducted from scratch (i.e. no initial labels/learners are needed) based on purely unsupervised criteria obtained from clusters: samples lying near cluster centers and near the borders of clusters are expected to represent the most informative ones regarding the distribution characteristics of the classes. In the second phase, the task is to update already trained classifiers during on-line mode with the most important samples in order to dynamically guide the classifier to more predictive power. Both strategies are essential for reducing the annotation and supervision effort of operators in off-line and on-line classification systems, as operators only have to label an exquisite subset of the off-line training data resp. give feedback only on specific occasions during on-line phase. The new active learning strategy is evaluated based on real-world data sets from UCI repository and collected at on-line quality control systems. The results show that an active learning based selection of training samples (1) does not weaken the classification accuracies compared to when using all samples in the training process and (2) can out-perform classifiers which are built on randomly selected data samples.