題名: A generalized version space learning algorithm for noisy and uncertain data
作者: T. P. Hong;S. S. Tseng
貢獻者: Department of Information Science and Applications 日期: 1997
上傳時間: 2009-11-30T08:03:10Z 出版者: Asia University
摘要: 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 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. ? 1997 IEEE.
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