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.

中文翻译：

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大数据背景下支持向量机算法的改进

随着互联网的飞速发展和大数据分析技术的飞速发展，数据挖掘对产业界和学术界起到了积极的推动作用。分类是数据挖掘中的一个重要问题。本文探讨了支持向量机（SVM）在数据挖掘分类算法中的背景和理论，分析总结了支持向量机各种改进方法的研究现状。根据数据的规模和特征，选择不同的解空间，将对偶问题的解转化为原始空间的分类面，提高算法速度。研究过程。将模糊隶属度纳入多核学习，发现原问题的时间复杂度由维度决定，对偶问题的时间复杂度由数量决定，维度和数量构成数据的尺度，所以可以根据数据的规模特征选择不同的解空间。通过将对偶问题的解转化为原始空间的分类面，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度，得出预测数据与实际值的拟合预测准确率高达98%，可以使传统机器学习算法满足大数据的要求。数据时代。它可以广泛应用于大数据的背景下。