论文标题
传统机器学习和数据和图像分类的深度学习技术的比较分析
Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification
论文作者
论文摘要
该研究的目的是分析和比较用于计算机视觉2D对象分类任务的最常见的机器学习和深度学习技术。首先，我们将介绍视觉单词模型和深层卷积神经网络（DCNN）的理论背景。其次，我们将实施一袋视觉单词模型，即VGG16 CNN体系结构。第三，我们将介绍我们的自定义和新手DCNN，其中我们在比利时交通标志数据集修改版本上测试上述实现。与经典的机器学习方法相比，我们的结果展示了超参数对传统机器学习的影响以及DCNNS准确性的优势。正如我们的测试所表明的那样，我们提出的解决方案可以比现有的DCNNS架构获得类似的结果，在某些情况下可以更好地实现结果。最后，本文的技术优点在于提出的计算更简单的DCNN体系结构，我们认为这可以为使用更有效的架构用于基本任务铺平道路。
The purpose of the study is to analyse and compare the most common machine learning and deep learning techniques used for computer vision 2D object classification tasks. Firstly, we will present the theoretical background of the Bag of Visual words model and Deep Convolutional Neural Networks (DCNN). Secondly, we will implement a Bag of Visual Words model, the VGG16 CNN Architecture. Thirdly, we will present our custom and novice DCNN in which we test the aforementioned implementations on a modified version of the Belgium Traffic Sign dataset. Our results showcase the effects of hyperparameters on traditional machine learning and the advantage in terms of accuracy of DCNNs compared to classical machine learning methods. As our tests indicate, our proposed solution can achieve similar - and in some cases better - results than existing DCNNs architectures. Finally, the technical merit of this article lies in the presented computationally simpler DCNN architecture, which we believe can pave the way towards using more efficient architectures for basic tasks.
