Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Stable Classification in Environments with Varying Degrees of Uncertainty
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Learning Gaussian Process Models from Uncertain Data
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Using uncertainty information to combine soft classifications
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
UNN: a neural network for uncertain data classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
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In supervised learning different sources of uncertainty influence the resulting functional behavior of the learning system which increases the risk of misbehavior. But still a learning system is often the only way to handle complex systems and large data sets. Hence it is important to consider the sources of uncertainty and to tackle them as far as possible. In this paper we categorize the sources of uncertainty and give a brief overview of uncertainty handling in supervised learning.