Computer Network Flow Recognition Method Based on Improved Support Vector Machine

Abstract

Computer network traffic recognition based on improved support vector machine is a defect of current mainstream network traffic algorithm, designed a network traffic prediction algorithm based on improved support vector machine. This paper mainly introduces the computer network traffic identification method based on improved support vector machine. This article mainly analyzes the related content of network traffic prediction, including the linear and nonlinear characteristics of network traffic, the theoretical basis of network traffic prediction and the method of obtaining traffic data. This paper studies support vector machine theory and least square support vector machine, and proposes an improved algorithm for least square support vector machine. The purpose of this article is to design a network traffic identification and analysis system. On the one hand, by monitoring the network traffic, we will be able to grasp the operation of the entire network in real time;on the other hand, the system statistically analyzes the results of different stages, We have a more comprehensive understanding of the operational efficiency of network resources, network performance and the rationality of network configuration. The experimental results in this paper show that the recognition efficiency of traffic based on the improved support vector machine method has been significantly improved. Under this method, the security problem of traffic has been increased by 14%, and the efficiency of traffic has been increased by 24%. The improved support vector machine will be the future computer network The development trend of traffic identification direction.

Flower Species Classify System Based on Deep Learning

“…Early approaches to flower classification used handcrafted features and traditional machine learning algorithms. For example, Wang and Ai [1] proposed a method that extracts shape, color, and texture features from flower images and uses support vector machines (SVMs) for classification. Similarly, Zhang and Wang [2] introduced a method that combines local and global features and uses random forests for classification.…”