Instance-Based Learning Algorithms
Machine Learning
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active Learning with Local Models
Neural Processing Letters
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Active Learning of the Generalized High-Low Game
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
The farthest point strategy for progressive image sampling
IEEE Transactions on Image Processing
Informed Selection of Training Examples for Knowledge Refinement
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Incremental learning with partial instance memory
Artificial Intelligence
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Learning to order BDD variables in verification
Journal of Artificial Intelligence Research
Cho-k-NN: a method for combining interacting pieces of evidence in case-based learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Index driven selective sampling for CBR
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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In the passive, traditional, approach to learning, the information available to the learner is a set of classified examples, which are randomly drawn from the instance space. In many applications, however, the initial classification of the training set is a costly process, and an intelligently selection of training examples from unlabeled data is done by an active learner.This paper proposes a lookahead algorithm for example selection and addresses the problem of active learning in the context of nearest neighbor classifiers. The proposed approach relies on using a random field model for the example labeling, which implies a dynamic change of the label estimates during the sampling process.The proposed selective sampling algorithm was evaluated empirically on artificial and real data sets. The experiments show that the proposed method outperforms other methods in most cases.