A New Hybrid Approach For Classification Problem

Imen Jammoussi, Mounir Ben Nasr
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Abstract

This work aims to introduce a new learning algorithm for classification issue. The suggested approach combine the self-organizing map (SOM) with the Extreme learning machine (ELM). In this algorithm, the load between input and hidden layers is made using the information retrieved from SOM on the training dataset. The weights of the output layer is adjusted applying an analytical method. Based on four classification benchmark, simulation results clarify that the new approach outperforms other learning algorithms and return sufficient performance in terms of learning speed and generalization. A comparative study with a number of other methods proves the efficiency of the proposed approach.
分类问题的一种新的混合方法
本文旨在为分类问题引入一种新的学习算法。该方法将自组织映射(SOM)与极限学习机(ELM)相结合。在该算法中,输入层和隐藏层之间的负载是使用从训练数据集上的SOM检索到的信息进行的。输出层的权重采用分析方法进行调整。基于四个分类基准,仿真结果表明,新方法在学习速度和泛化方面优于其他学习算法,并返回足够的性能。通过与其他几种方法的比较研究,证明了该方法的有效性。
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