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Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
March-April 1997 (vol. 9 no. 2)
pp. 336-340

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.

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Index Terms:
Machine learning, version space, multiple version spaces, noise, uncertainty, training instance.
Citation:
Tzung-Pei Hong, Shian-Shyong Tseng, "Generalized Version Space Learning Algorithm for Noisy and Uncertain Data," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 2, pp. 336-340, March-April 1997, doi:10.1109/69.591457
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