Purpose

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

Design/methodology/approach

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

Findings

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

Research limitations/implications

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

Practical implications

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

Originality/value

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

中文翻译：

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基于新型支持向量机算法的第三方物流大数据应用研究

目的

本文旨在有效探索基于α-支持向量机-随机梯度下降（SVMSGD）算法的大数据技术在第三方物流中的应用效果，获取隐藏在物流大数据中的有价值信息，促进物流企业的发展。制定更合理的规划方案。

设计/方法/方法

本文在不改变算法复杂度的情况下引入遗忘因子，提出了一种基于遗忘因子的算法，称为α -SVMSGD算法。该算法选择性地删除或保留历史数据，提高了分类器对实时新物流数据的适应性。仿真结果验证了算法的应用效果。

发现

随着训练次数的增加，梯度下降（GD）算法、梯度下降支持（SGD）算法和α -SVMSGD算法的测试错误率逐渐降低；在物流大数据处理过程中，α -SVMSGD算法在保证GD方向接近最优解方向的同时，具有SGD算法的效率，可以利用少量数据获得更准确的结果，提高收敛精度。

研究限制/影响

遗忘因子的阈值设置仍需改进。为自学习中的不同数据类型设置阈值已成为研究方向。通过大数据处理技术可以有效控制遗忘数据的数量，提高对第三方物流正常运营的数据支撑。

实际影响

能有效降低数据挖掘的耗时，在不增加样本复杂度的情况下实现样本数据的快速准确收敛，提高物流大数据挖掘效率，减少历史数据冗余，具有一定的参考价值促进物流业发展。

原创性/价值

本文提出的分类算法在第三方物流大数据挖掘中具有可行性和高收敛性。本文提出的α -SVMSGD算法在实时物流数据挖掘中具有一定的应用价值，但遗忘因子阈值的设计有待改进。未来，作者将继续研究如何在自学习中设置不同的数据类型阈值。