中文摘要：

极限学习机（ELM）在机器学习领域获得了很多的关注,并在应用方面取得了极大的成功。然而,极限学习机对训练数据中的异常值点和非高斯噪声非常敏感,从而大大阻碍了ELM的应用。概率权重ELM方法主要对含有异常值和非高斯噪声数据集进行建模,首先建立概率局部ELM模型,并在此基础上利用Parzen窗方法建立局部模型的概率分布,然后将概率分布作为权重来融合所有的局部模型以建立全局鲁棒性模型。该方法成功地应用了数学例子和UCI实例,并与传统ELM、正则化ELM和鲁棒ELM进行了比较分析,结果表明概率权重ELM表现出了较好的建模性能。

英文摘要：

Extreme learning machine（ELM）has attracted a lot of attention in the machine learning field and gained great success in application.However,it is sensitive to outliers and non-Gaussian noise in the training dataset,which greatly hinder the application of ELM.Probabilistic weighted ELM was proposed to model the dataset with the outliers and non-Gaussian noise.First,a distributed local ELM modeling is developed,upon which the probability distribution function（PDF）of multiple local models is estimated by the Parzen window method.Then,the distribution function is further used as weights to integrate all local models to construct a global robust ELM model.The successful application of this robust probabilistic weighted ELM method to both artificial case and real life case as well as its comparison to traditional ELM,regularization ELM and robust ELM demonstrate its superiority in the modeling.