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<p>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.</p>
learning (artificial intelligence); parallel algorithms; computational complexity; generalisation (artificial intelligence); configuration management; concept learning; parallel version-space learning algorithm; divide-and-conquer method; time complexity; training instances; application domains; bounded processor number; generalization process; hypothesis; specialization process

T. Hong and S. Tseng, "Learning Concepts in Parallel Based Upon the Strategy of Version Space," in IEEE Transactions on Knowledge & Data Engineering, vol. 6, no. , pp. 857-867, 1994.
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