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Improvement of Support Vector Machine Algorithm in Big Data Background

机译：大数据背景中支持向量机算法的改进

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摘要

With the rapid development of the Internet and the rapid development of big data analysis technology, data mining has played a positive role in promoting industry and academia. Classification is an important problem in data mining. This paper explores the background and theory of support vector machines (SVM) in data mining classification algorithms and analyzes and summarizes the research status of various improved methods of SVM. According to the scale and characteristics of the data, different solution spaces are selected, and the solution of the dual problem is transformed into the classification surface of the original space to improve the algorithm speed. Research Process. Incorporating fuzzy membership into multicore learning, it is found that the time complexity of the original problem is determined by the dimension, and the time complexity of the dual problem is determined by the quantity, and the dimension and quantity constitute the scale of the data, so it can be based on the scale of the data Features Choose different solution spaces. The algorithm speed can be improved by transforming the solution of the dual problem into the classification surface of the original space. Conclusion . By improving the calculation rate of traditional machine learning algorithms, it is concluded that the accuracy of the fitting prediction between the predicted data and the actual value is as high as 98%, which can make the traditional machine learning algorithm meet the requirements of the big data era. It can be widely used in the context of big data.
机译：随着互联网的快速发展和大数据分析技术的快速发展，数据挖掘在促进行业和学术界方面发挥了积极作用。分类是数据挖掘中的一个重要问题。本文探讨了数据挖掘分类算法中的支持向量机（SVM）的背景和理论，并分析并总结了各种改进的SVM方法的研究状态。根据数据的规模和特性，选择不同的解决方案空间，并将双重问题的解决方案转换为原始空间的分类表面以提高算法速度。研究过程。将模糊会员资格纳入多核学习，发现原始问题的时间复杂度由维度决定，并且双问题的时间复杂度由数量确定，维度和数量构成数据的比例，因此，它可以基于数据功能的规模选择不同的解决方案空间。通过将双重问题的解决方案转换为原始空间的分类表面，可以提高算法速度。结论 。通过提高传统机器学习算法的计算速率，得出结论，预测数据与实际值之间的拟合预测的准确性高达98％，这可以使传统的机器学习算法满足大的要求数据时代。它可以广泛使用在大数据的背景下。

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