標題: LEARNING CONCEPTS IN PARALLEL BASED UPON THE STRATEGY OF VERSION SPACE
作者: HONG, TP
TSENG, SS
資訊工程學系
Department of Computer Science
關鍵字: DIVIDE-AND-CONQUER;GENERALIZATION PROCESS;HYPOTHESIS;PARALLEL LEARNING;SPECIALIZATION PROCESS;TRAINING INSTANCE;VERSION SPACE
公開日期: 1-Dec-1994
摘要: In this paper, we have attempted to apply 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(2) n) with n processors, where n is the number of given training instances and k is a coefficient depending on application domains. For a bounded number of processors in the real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing our parallel learning algorithm works and being quite consistent with results of theoretic analysis. We have finally concluded that when the number of training instances is large, it is worth learning in parallel because of its faster execution.
URI: http://dx.doi.org/10.1109/69.334877
http://hdl.handle.net/11536/2206
ISSN: 1041-4347
DOI: 10.1109/69.334877
期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume: 6
Issue: 6
起始頁: 857
結束頁: 867
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  1. A1994PT15400001.pdf