Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Hyperrelations in version space
Proceedings of the 2002 ACM symposium on Applied computing
A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making
Applied Intelligence
Machine Learning on the Basis of Formal Concept Analysis
Automation and Remote Control
Learning Concepts by Arranging Appropriate Training Order
Minds and Machines
Fuzzy Inductive Learning Strategies
Applied Intelligence
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery with second-order relations
Knowledge and Information Systems
Learning premises of fuzzy rules for knowledge acquisition in classification problems
Knowledge and Information Systems
Geography of Differences between Two Classes of Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining as Constraint Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
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
Version Space Learning with DNA Molecules
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Demand-Driven Construction of Structural Features in ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Version spaces and the consistency problem
Artificial Intelligence
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structural geography of the space of emerging patterns
Intelligent Data Analysis
SPHINX: Schema integration by example
Journal of Intelligent Information Systems
Determining appropriate membership functions to simplify fuzzy induction
Intelligent Data Analysis
Concept Learning from (Very) Ambiguous Examples
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Extracting Decision Correlation Rules
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Although a landmark work, version spaces have proven fundamentally limited by being constrained to only consider candidate classifiers that are strictly consistent with data. This work generalizes version spaces to partially overcome this limitation. The main insight underlying this work is to base learning on version-space intersection, rather than the traditional candidate-elimination algorithm. The resulting learning algorithm, incremental version-space merging (IVSM), allows version spaces to contain arbitrary sets of classifiers, however generated, as long as they can be represented by boundary sets. This extends version spaces by increasing the range of information that can be used in learning; in particular, this paper describes how three examples of very different types of information—ambiguous data, inconsistent data, and background domain theories as traditionally used by explanation-based learning—can each be used by the new version-space approach.