Abstract
In recent years, extreme learning machine (ELM) and its improved algorithms have been successfully applied to various classification and regression tasks. In these algorithms, MSE criterion is commonly used to control training error. However, MSE criterion is not suitable to deal with outliers, which can exist in general regression or classification tasks. In this paper, a novel extreme learning machine under minimum information divergence criterion (ELM-MinID) is proposed to deal with the training set with noises. In minimum information divergence criterion, the Gaussian kernel function and Euclidean information divergence are utilized to substitute the mean square error (MSE) criterion to enhance the anti-noise ability of ELM. Experimental results on two synthetic datasets and eleven benchmark datasets show that this method is superior to traditional ELMs.
Highlights
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN) with universal approximation capability [1], [2]
The corresponding weights linking the hidden layers to the output layers can be directly determined by the least square method based on the MoorePenrose generalized inverse [3]
The minimum information divergence (MinID) criterion based on Euclidean information divergence is applied to extreme learning machine (ELM)
Summary
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN) with universal approximation capability [1], [2]. MSE mainly focuses on the scatter aspects of the error distribution and cannot draw all the probabilistic information of the error, such as the shape (kurtosis, tails, peaks, etc.) of probability density function To address this issue, Chen et al [14]–[16] proposed a novel minimum information divergence (MinID) criterion, in which the Kullback-Leibler divergence between the actual error and. Song et al.: ELM-MinID the desired error is selected as the objective function for adaptation algorithm This criterion has been successfully used in adaptive filtering. In order to overcome the defects of above ELMs and improve the anti-noise ability of ELM, a novel ELM-MinID algorithm is developed in this paper In this algorithm, the MinID criterion based on Euclidean information divergence is applied to extreme learning machine (ELM).
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