ABSTRACT The exact evaluation of Extreme Learning Machine (ELM) compactness is difficult due to the randomness in hidden layer nodes number, weight and bias values. To overcome this randomness, and other problems such as resultant overfitting and large variance, a selective weighted voting ensemble model based on regularized ELM is investigated. It can strongly enhance the overall performance including accuracy, variance and time consumption. Efficient Prediction Sum of Squares (PRESS) criteria that utilizing Singular Value Decomposition (SVD) is proposed to address the slow execution. Furthermore, an ensemble pruning approach based on the eigenvalues for the input weight matrix is developed. In this work, the ensemble base classifiers weights are calculated based on the same PRESS error metric used for the solutions of the output weight vector (β) in RELM, thus, it can reduce computational cost and space requirement. Different state-of-the-art learning approaches and various well-known facial expressions faces and object recognition benchmark datasets were examined in this work.

中文翻译：

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高效正则化极限学习机的加权投票集成

摘要 由于隐藏层节点数量、权重和偏差值的随机性，极限学习机 (ELM) 紧凑性的准确评估是困难的。为了克服这种随机性以及其他问题，例如过度拟合和大方差，研究了一种基于正则化 ELM 的选择性加权投票集成模型。它可以极大地提高整体性能，包括准确性、方差和时间消耗。提出了利用奇异值分解（SVD）的有效预测平方和（PRESS）标准来解决执行缓慢的问题。此外，还开发了一种基于输入权重矩阵特征值的集成剪枝方法。在这项工作中，集成基分类器的权重基于与 RELM 中输出权重向量 (β) 的解相同的 PRESS 误差度量计算，因此可以降低计算成本和空间需求。在这项工作中检查了不同的最先进的学习方法和各种著名的面部表情和对象识别基准数据集。