標題: A generalized version space learning algorithm for noisy and uncertain data
作者: Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: machine learning;version space;multiple version spaces;noise;uncertainty;training instance
公開日期: 1-三月-1997
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 phase 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.
URI: http://dx.doi.org/10.1109/69.591457
http://hdl.handle.net/11536/667
ISSN: 1041-4347
DOI: 10.1109/69.591457
期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume: 9
Issue: 2
起始頁: 336
結束頁: 340
顯示於類別:期刊論文


文件中的檔案:

  1. A1997WV23100012.pdf