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
Robust Classification for Imprecise Environments
Machine Learning
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Optimising area under the ROC curve using gradient descent
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Exploiting AUC for optimal linear combinations of dichotomizers
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Linear model combining by optimizing the Area under the ROC curve
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Active learning for outdoor perception
Active learning for outdoor perception
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
A stopping criterion for active learning
Computer Speech and Language
Maximizing area under ROC curve for biometric scores fusion
Pattern Recognition
Optimization of the Area under the ROC Curve
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
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Aiming at labeling and ranking difficulties caused by a large number of samples, as well as uneven distribution of samples in outdoor obstacle detection of the autonomous mobile robot, an AUC maximization linear classifier method based on active learning is proposed in this paper. This method firstly uses dynamic clustering algorithm to select the representative samples and labels these samples, then these labeled samples are put in the training set. Next, a linear classifier is trained using the AUC maximization method on the training set. The above process will be repeated until the AUC converges. The experiments are performed on real outdoor environment image database. The experiment results show that the very good detection results are obtained using the method proposed in this paper with only 120 samples. More importantly, using the proposed method can significantly reduce the workload of labeling the samples and size of the sample set, and AUC maximization proposed also excels the existing methods.