Online sequential reduced kernel extreme learning machine
Online sequential reduced kernel extreme learning machine
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在线顺序缩减核极限学习机
DOI：
10.1016/j.neucom.2015.06.087
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发表时间：
2016-01-22
期刊：
影响因子：
6
通讯作者：
Zheng, Qing-Hua
中科院分区：
文献类型：
--
作者：
Deng, Wan-Yu;Ong, Yew-Soon;Zheng, Qing-Hua
提出了一种在线序贯约简核极值学习机(OS-RKELM)。在OS-RKELM中，只使用原始训练样本中的一小部分实例来训练核神经元，而输出权值是通过解析获得的。与在线序列极限学习机(OS-ELM)类似，OS-RKELM以逐块或逐个模式学习数据样本，并且在学习数据样本后不需要对其进行存档。OS-RKELM也包含很少的控制参数，从而避免了对算法进行繁琐的微调。OS-RKELM支持多种类型的核作为隐含神经元，能够解决初始训练样本小于神经元大小时出现的奇异问题。使用流行的时间序列、回归和分类基准对OS-RKELM与其他最先进的顺序学习算法进行了全面的性能评估，这些算法包括OS-ELM、大规模主动支持向量机(LASVM)和预算随机梯度下降支持向量机(BSGD)。实验结果表明，OS-RKELM在很多情况下都比OS-ELM、LASVM和BSGD具有更高的预测精度和效率。(C)2015爱思唯尔B.V.保留所有权利。
In this paper, we present an Online Sequential Reduced Kernel Extreme Learning Machine (OS-RKELM). In OS-RKELM, only a small part of the instances in the original training samples is employed for training the kernel neurons, while the output weights are attained analytically. Similar to the Online Sequential Extreme Learning Machine (OS-ELM), OS-RKELM learns data samples in a chunk-by-chunk or one-by-one mode and does not require an archival of the data sample once it has been learned. OS-RKELM also contains few control parameters, thus avoiding the need for cumbersome fine-tuning of the algorithm. OS-RKELM supports a widespread types of kernels as hidden neurons and is capable of addressing the singular problem that arises when the initial training samples are smaller than the neuron size. A comprehensive performance evaluation of the OS-RKELM against other state-of-the-art sequential learning algorithms, including OS-ELM, Large-scale Active Support Vector Machine (LASVM) and Budgeted Stochastic Gradient Descent Support Vector Machine (BSGD) using popular time series, regression and classification benchmarks have been conducted. Experimental results obtained indicate that the proposed OS-RKELM showcases improved prediction accuracy and efficiency over the OS-ELM, LASVM and BSGD in many cases. (C) 2015 Elsevier B.V. All rights reserved.