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Semisupervised learning in pattern recognition with concept drift

机译：带有概念漂移的模式识别中的半监督学习

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摘要

We describe the use of semisupervised learning to adapt decision rule in the pattern recognition problem with concept drift. There has been generated a new classifier using additional dataset that becomes available from the changing environment. The classifier is a combined cluster structure with a modified weighted Bayesian decision rule, where the weights are dynamically updated using the classifier’s current decision. The probability density functions are identified in each cluster and the decision rule is defined as a distribution mixture. The adaptation mechanism allows the algorithm to track the environment changes by weighting the most recent and relevant cluster higher. The adaptive algorithm is described, and its performance is compared to the static one by using specific model problem.
机译：我们描述了在概念漂移的模式识别问题中使用半监督学习来适应决策规则。使用附加的数据集已经生成了一个新的分类器，该数据集可以从不断变化的环境中获得。分类器是具有改进的加权贝叶斯决策规则的组合聚类结构，其中权重使用分类器的当前决策动态更新。在每个聚类中确定概率密度函数，并将决策规则定义为分布混合。自适应机制允许算法通过对最新的和相关的簇进行加权来跟踪环境变化。描述了自适应算法，并通过使用特定模型问题将其性能与静态算法进行了比较。

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