An anytime approach to connectionist theory refinement: refining the topologies of knowledge-based neural networks
Anytime algorithm development tools
ACM SIGART Bulletin
Communications of the ACM
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Modern Information Retrieval
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Induction By Attribute Elimination
IEEE Transactions on Knowledge and Data Engineering
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Approach to Anytime Learning
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Scheduling contract algorithms on multiple processors
Eighteenth national conference on Artificial intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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In many online applications of machine learning, the computational resources available for classification will vary from time to time. Most techniques are designed to operate within the constraints of the minimum expected resources and fail to utilize further resources when they are available. We propose a novel anytime classification algorithm, anytime averaged probabilistic estimators (AAPE), which is capable of delivering strong prediction accuracy with little CPU time and utilizing additional CPU time to increase classification accuracy. The idea is to run an ordered sequence of very efficient Bayesian probabilistic estimators (single improvement steps) until classification time runs out. Theoretical studies and empirical validations reveal that by properly identifying, ordering, invoking and ensembling single improvement steps, AAPE is able to accomplish accurate classification whenever it is interrupted. It is also able to output class probability estimates beyond simple 0/1-loss classifications, as well as adeptly handle incremental learning.