基于GPU的极限学习机分类

Toma Jeowicz, P. Gajdoš, Vojtěch Uher, V. Snás̃el
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引用次数: 7

摘要

一般分类是一种机器学习任务,它试图根据过去的观察(训练数据)将最佳类分配给给定的未知输入向量。大多数已开发的算法对于大型数据集(支持向量机,深度神经网络等)都非常耗时。极限学习机(Extreme Learning Machine, ELM)是近年来得到广泛应用的一种高质量分类算法。研究表明,利用GPU平台可以提高该算法的学习速度。实验结果表明,该方法的速度更快,并且具有与原始ELM算法相同的精度。所提出的方法完全运行在GPU平台上,因此它可以有效地集成到其他应用中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification with Extreme Learning Machine on GPU
The general classification is a machine learning task that tries to assign the best class to a given unknown input vector based on past observations (training data). Most of developed algorithms are very time consuming for large datasets (Support Vector Machine, Deep Neural Networks, etc.). Extreme Learning Machine (ELM) is a high quality classification algorithm that gains much popularity in recent years. This paper shows that the speed of learning of this algorithm may be improved by using GPU platform. Experimental results showed that proposed approach is much faster and provides the same accuracy as the original ELM algorithm. The proposed approach runs completely on GPU platform and thus it may be effectively incorporated within other applications.
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