An analytical comparison of some rule-learning programs
Artificial Intelligence
Machine learning of inductive bias
Machine learning of inductive bias
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 4
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Error correction in constructive induction
Proceedings of the sixth international workshop on Machine learning
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Shift of bias without operators
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Version Space Algorithms on Hierarchies with Exceptions
EPIA '93 Proceedings of the 6th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Mining fuzzy rules from quantitative data based on the AprioriTid algorithm
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Learning Concepts by Arranging Appropriate Training Order
Minds and Machines
Fuzzy Inductive Learning Strategies
Applied Intelligence
Splitting and Merging Version Spaces to Learn Disjunctive Concepts
IEEE Transactions on Knowledge and Data Engineering
Learning premises of fuzzy rules for knowledge acquisition in classification problems
Knowledge and Information Systems
Construction of Efficient Rulesets from Fuzzy Data through Simulated Annealing
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
Knowledge acquisition from quantitative data using the rough-set theory
Intelligent Data Analysis
VODKA: Variant objects discovering knowledge acquisition
Expert Systems with Applications: An International Journal
Contextualized Recommendation Based on Reality Mining From Mobile Subscribers
Cybernetics and Systems
Expert Systems with Applications: An International Journal
Mining from incomplete quantitative data by fuzzy rough sets
Expert Systems with Applications: An International Journal
Discovering fuzzy inter- and intra-object associations
Expert Systems with Applications: An International Journal
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This paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning phase then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical.