Title: A generalized version space learning algorithm for noisy and uncertain data
Authors: Hong, TP
Tseng, SS
Department of Computer Science
Keywords: machine learning;version space;multiple version spaces;noise;uncertainty;training instance
Issue Date: 1-Mar-1997
Abstract: 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
ISSN: 1041-4347
DOI: 10.1109/69.591457
Volume: 9
Issue: 2
Begin Page: 336
End Page: 340
Appears in Collections:Articles

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  1. A1997WV23100012.pdf