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<p><b>Abstract</b>—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: <it>searching</it> and <it>pruning</it>. 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.</p>
Machine learning, version space, multiple version spaces, noise, uncertainty, training instance.

S. Tseng and T. Hong, "Generalized Version Space Learning Algorithm for Noisy and Uncertain Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 9, no. , pp. 336-340, 1997.
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