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A generalized version space learning algorithm for noisy and uncertain data

机译:噪声和不确定数据的广义版本空间学习算法

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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.
机译:本文概括了版本空间的学习策略,以管理嘈杂和不确定的训练数据。提出了一种新的学习算法,该算法包括两个主要阶段:搜索和修剪。搜索阶段生成可能的候选者并将其收集到一个大集合中;然后,修剪将根据各种条件修剪此集合,以找到最大一致的版本空间。当训练实例不能完全分类时,根据不同应用领域的需求,所提出的学习算法可以在包括正训练实例和排除负训练实例之间进行权衡。此外,根据给定的时间限制选择合适的修剪参数,因此该算法还可以在时间复杂度和准确性之间进行权衡。所提出的学习算法是一种灵活高效的归纳方法,使版本空间学习策略更加实用。

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