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
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A lazy bagging approach to classification
Pattern Recognition
Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Anytime classification for a pool of instances
Machine Learning
Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
BT*: an advanced algorithm for anytime classification
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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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.