基于虚拟现实技术的数字显示屏视觉设计与改进型 SVM 算法

Hanzhuo Zuo
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摘要

简介:随着虚拟现实(VR)技术的快速发展,数字显示在各个领域变得越来越重要。本研究旨在通过改进支持向量机(SVM)算法,提高虚拟现实技术在数字显示屏视觉设计中的应用。数字显示屏的视觉设计对于吸引用户、增强体验和传递信息至关重要,因此需要一种准确可靠的算法来支持相关决策。目标:本研究旨在改进 SVM 算法,以更准确地识别与数字显示屏视觉设计相关的特征。通过利用 SVM 算法的非线性映射和参数优化,旨在提高模型的性能,使其能更好地适应复杂的视觉设计场景。方法:在实现目标的过程中,首先收集了与数字显示相关的多媒体数据,包括图像和视频。通过特征工程,选择与视觉设计密切相关的特征,并应用深度学习技术提取更高层次的特征表征。随后,改进了 SVM 算法,使用核函数进行非线性映射,并调整了惩罚参数和核函数参数。在模型的训练和测试阶段使用了交叉验证,以确保其泛化性能。结果:通过在测试集上进行评估,改进后的 SVM 算法与传统方法相比表现出更高的准确率、召回率和精确度。这表明该模型能够更准确地捕捉数字显示中的视觉设计特征,并为相关决策提供更可靠的支持。结论:本研究表明,通过改进 SVM 算法,可以在虚拟现实技术的数字显示中实现更准确的视觉设计。这一改进为数字显示的设计提供了可靠的算法支持,并为用户提供了更加丰富的沉浸式体验。未来的研究可以进一步优化算法,并根据用户反馈不断改进虚拟现实环境中数字显示的视觉设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm
NTRODUCTION: With the rapid development of virtual reality (VR) technology, digital displays have become increasingly important in various fields. This study aims to improve the application of virtual reality technology in the visual design of digital displays by improving the support vector machine (SVM) algorithm. The visual design of digital displays is crucial for attracting users, enhancing experience and conveying information, so an accurate and reliable algorithm is needed to support relevant decisions. OBJECTIVES: The purpose of this study is to improve the SVM algorithm to more accurately identify features related to the visual design of digital displays. By exploiting the nonlinear mapping and parameter optimization of the SVM algorithm, it aims to improve the performance of the model so that it can better adapt to complex visual design scenarios. METHODS: In the process of achieving the objective, multimedia data related to digital displays, including images and videos, were first collected. Through feature engineering, features closely related to visual design were selected, and deep learning techniques were applied to extract higher-level feature representations. Subsequently, the SVM algorithm was improved to use the kernel function for nonlinear mapping, and the penalty parameters and the parameters of the kernel function were adjusted. Cross-validation was used in the training and testing phases of the model to ensure its generalization performance. RESULTS: The improved SVM algorithm demonstrated higher accuracy, recall and precision compared to the traditional method by evaluating it on the test set. This suggests that the model is able to capture visual design features in digital displays more accurately and provide more reliable support for relevant decisions. CONCLUSION: This study demonstrates that by improving the SVM algorithm, more accurate visual design can be achieved in digital displays of virtual reality technology. This improvement provides reliable algorithmic support for the design of digital displays and provides a more prosperous, immersive experience for users. Future research can further optimize the algorithm and iterate with user feedback to continuously improve the visual design of digital displays in virtual reality environments.
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