Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
Original Contribution: Stacked generalization
Neural Networks
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Implementing inner drive through competence reflection
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Transduction with Confidence and Credibility
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Making Sensitivity Analysis Computationally Efficient
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Data perturbation for escaping local maxima in learning
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructive Incremental Learning from Only Local Information
Neural Computation
Estimation of individual prediction reliability using the local sensitivity analysis
Applied Intelligence
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Single-stacking conformity approach to reliable classification
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
k-version-space multi-class classification based on k-consistency tests
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Quantifying the reliability of fault classifiers
Information Sciences: an International Journal
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In Machine Learning, estimation of the predictive accuracy for a given model is most commonly approached by analyzing the average accuracy of the model. In general, the predictive models do not provide accuracy estimates for their individual predictions. The reliability estimates of individual predictions require the analysis of various model and instance properties. In the paper we make an overview of the approaches for estimation of individual prediction reliability. We start by summarizing three research fields, that provided ideas and motivation for our work: (a) approaches to perturbing learning data, (b) the usage of unlabeled data in supervised learning, and (c) the sensitivity analysis. The main part of the paper presents two classes of reliability estimation approaches and summarizes the relevant terminology, which is often used in this and related research fields.