Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Inductive Confidence Machines for Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Pattern Recognition and Density Estimation under the General i.i.d. Assumption
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
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In this paper we propose a new algorithm for providing confidence and credibility values for predictions on a multi-class pattern recognition problem which uses Support Vector machines in its implementation. Previous algorithms which have been proposed to achieve this are very processing intensive and are only practical for small data sets. We present here a method which overcomes these limitations and can deal with larger data sets (such as the US Postal Service database). The measures of confidence and credibility given by the algorithm are shown empirically to reflect the quality of the predictions obtained by the algorithm, and are comparable to those given by the less computationally efficient method. In addition to this the overall performance of the algorithm is shown to be comparable to other techniques (such as standard Support Vector machines), which simply give flat predictions and do not provide the extra confidence/credibility measures.