Robust regression and outlier detection
Robust regression and outlier detection
The Strength of Weak Learnability
Machine Learning
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Dynamic Programming
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
High breakdown mixture discriminant analysis
Journal of Multivariate Analysis
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Object localization by subspace clustering of local descriptors
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weakly-Supervised Classification with Mixture Models for Cervical Cancer Detection
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Expert Systems with Applications: An International Journal
Technical Section: Neural network-based symbol recognition using a few labeled samples
Computers and Graphics
Label noise-tolerant hidden Markov models for segmentation: application to ECGs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA
Pattern Recognition Letters
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
Estimating mutual information for feature selection in the presence of label noise
Computational Statistics & Data Analysis
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In the supervised classification framework, human supervision is required for labeling a set of learning data which are then used for building the classifier. However, in many applications, human supervision is either imprecise, difficult or expensive. In this paper, the problem of learning a supervised multi-class classifier from data with uncertain labels is considered and a model-based classification method is proposed to solve it. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Experiments on artificial and real data are provided to highlight the main features of the proposed method as well as an application to object recognition under weak supervision.