Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Variance and Bias for General Loss Functions
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Bias and variance of rotation-based ensembles
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The successful application of machine learning techniques to industrial problems places various demands on the collaborators. The system designers must possess appropriate analytical skills and technical expertise, and the management of the industrial or commercial partner must be sufficiently convinced of the potential benefits that they are prepared to invest in money and equipment. Vitally, the collaboration also requires a significant investment in time from the end-users in order to provide training data from which the system can (hopefully) learn. This poses a problem if the developed Machine Learning system is not sufficiently accurate, as the users and management may view their input as wasted effort, and lose faith with the process. In this paper we investigate techniques for making early predictions of the error rate achievable after further interactions. In particular we show how decomposing the error in different components can lead to useful predictors of achievable accuracy, but that this is dependent on the choice of an appropriate sampling methodology.