The nature of statistical learning theory
The nature of statistical learning theory
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Comparing the Bayes and Typicalness Frameworks
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Transductive Confidence Machines for Pattern Recognition
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
Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Computationally Efficient Transductive Machines
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Normalized nonconformity measures for regression Conformal Prediction
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Reliable Predictive Intervals for the Critical Frequency of the F2 Ionospheric Layer
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
Mining tolerance regions with model trees
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Reliable probabilistic classification with neural networks
Neurocomputing
| Hi-index | 0.01 |
The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial solutions have been suggested in the past. Both the original method and these solutions were based on transductive inference. In this paper we make a radical step of replacing transductive inference with inductive inference and define what we call the Inductive Confidence Machine (ICM); our main concern in this paper is the use of ICM in regression problems. The algorithm proposed in this paper is based on the Ridge Regression procedure (which is usually used for outputting bare predictions) and is much faster than the existing transductive techniques. The inductive approach described in this paper may be the only option available when dealing with large data sets.