Communications of the ACM
Empirical model-building and response surface
Empirical model-building and response surface
Learning regular sets from queries and counterexamples
Information and Computation
On the sample complexity of pac-learning using random and chosen examples
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Advances in neural information processing systems 2
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
Active Learning Using Arbitrary Binary Valued Queries
Machine Learning
Theory and Practice of Vector Quantizers Trained on Small Training Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedagogical pattern selection strategies
Neural Networks
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Machine Learning
Experience with selecting exemplars from clean data
Neural Networks
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Neural network exploration using optimal experiment design
Neural Networks
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Selecting optimal experiments for multiple output multilayer perceptrons
Neural Computation
Self-organizing maps
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active Learning with Local Models
Neural Processing Letters
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An incremental nearest neighbor algorithm with queries
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Exploration in active learning
The handbook of brain theory and neural networks
A stochastic self-organizing map for proximity data
Neural Computation
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Input Selection with Partial Retraining
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Learning and generalization in cascade network architectures
Neural Computation
Active learning with statistical models
Journal of Artificial Intelligence Research
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Cross-validation with active pattern selection for neural-network classifiers
IEEE Transactions on Neural Networks
A hierarchical RBF online learning algorithm for real-time 3-D scanner
IEEE Transactions on Neural Networks
Reinforcement learning using a grid based function approximator
Biomimetic Neural Learning for Intelligent Robots
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We discuss a new paradigm, called active learning, for supervised learning that aims at improving the efficiency of neural network training procedures. The starting point for active learning is the observation that the traditional approach of randomly selecting training samples leads to large, highly redundant training sets. This redundancy is not always desirable. Especially if the acquisition of training data is expensive, one is rather interested in small, information training sets. Such training sets can be obtained if the learner is enabled to select those training data that he or she expects to be most informative. In this case, the learner is no longer a passive recipient of information but takes an active role in the selection of the training data.