An analytical comparison of some rule-learning programs
Artificial Intelligence
Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Designing efficient algorithms for parallel computers
Designing efficient algorithms for parallel computers
Applying inductive learning to enhance knowledge-based expert systems
Decision Support Systems
Using concept learning for knowledge acquisition
International Journal of Man-Machine Studies
The design and analysis of parallel algorithms
The design and analysis of parallel algorithms
Incremental version-space merging: a general framework for concept learning
Incremental version-space merging: a general framework for concept learning
Fundamentals of Computer Alori
Fundamentals of Computer Alori
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
A New Probabilistic Induction Method
Journal of Automated Reasoning
Learning Concepts by Arranging Appropriate Training Order
Minds and Machines
Splitting and Merging Version Spaces to Learn Disjunctive Concepts
IEEE Transactions on Knowledge and Data Engineering
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Applies the technique of parallel processing to concept learning. A parallel version-space learning algorithm based upon the principle of divide-and-conquer is proposed. Its time complexity is analyzed to be O(k log/sub 2/n) with n processors, where n is the number of given training instances and k is a coefficient depending on the application domains. For a bounded number of processors in real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing that our parallel learning algorithm works, and being quite consistent with the results of theoretical analysis. We conclude that when the number of training instances is large, it is worth learning in parallel because of its faster execution.