Suppr超能文献

微学习支持向量机模式分类:一种高速算法。

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm.

机构信息

School of Economics, Peking University, Haidian District, Beijing 100871, China.

Key Laboratory of Mathematical Economics and Quantitative Finance, Peking University, Haidian District, Beijing 100871, China.

出版信息

Comput Intell Neurosci. 2022 Aug 3;2022:4707637. doi: 10.1155/2022/4707637. eCollection 2022.

Abstract

The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is ( ) time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

摘要

目前,支持向量机理论已经发展成为一个非常成熟的系统。本文将原始的支持向量机求解优化问题转化为直线的直接计算公式,模型的()时间复杂度。在本文的模型中,weited 理论、多分类问题和在线学习都成为了直接推断,我们已经将新模型应用于 UCI 数据集。我们希望在未来,这个模型将在股票预测等需要非线性高速算法的现实问题中发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9749/9365542/911d5c095c6a/CIN2022-4707637.001.jpg

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