The nature of statistical learning theory
The nature of statistical learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Bump hunting in high-dimensional data
Statistics and Computing
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
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Convergence and Application of Online Active Sampling Using Orthogonal Pillar Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-Based Classifier Design with Controlled Confidence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
An empirical study of active learning with support vector machines for Japanese word segmentation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Confidence-based classifier design
Pattern Recognition
Active learning with statistical models
Journal of Artificial Intelligence Research
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Reliability estimation of a statistical classifier
Pattern Recognition Letters
A stopping criterion for active learning
Computer Speech and Language
Improving supervised learning performance by using fuzzy clustering method to select training data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Efficient Coverage of Case Space with Active Learning
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Confidence-based stopping criteria for active learning for data annotation
ACM Transactions on Speech and Language Processing (TSLP)
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
d-Confidence: an active learning strategy which efficiently identifies small classes
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Consensus of ambiguity: theory and application of active learning for biomedical image analysis
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
Active learning for sparse least squares support vector machines
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
Inconsistency-based active learning for support vector machines
Pattern Recognition
Active labeling application applied to food-related object recognition
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
Active learning for on-road vehicle detection: a comparative study
Machine Vision and Applications
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This paper proposes a new active learning approach, confidence-based active learning, for training a wide range of classifiers. This approach is based on identifying and annotating uncertain samples. The uncertainty value of each sample is measured by its conditional error. The approach takes advantage of current classifiers' probability preserving and ordering properties. It calibrates the output scores of classifiers to conditional error. Thus, it can estimate the uncertainty value for each input sample according to its output score from a classifier and select only samples with uncertainty value above a user-defined threshold. Even though we cannot guarantee the optimality of the proposed approach, we find it to provide good performance. Compared with existing methods, this approach is robust without additional computational effort. A new active learning method for support vector machines (SVMs) is implemented following this approach. A dynamic bin width allocation method is proposed to accurately estimate sample conditional error and this method adapts to the underlying probabilities. The effectiveness of the proposed approach is demonstrated using synthetic and real data sets and its performance is compared with the widely used least certain active learning method.