卷积神经网络在图像分类中的分类器比较

2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) Pub Date : 2019-10-01 DOI:10.1109/DS-RT47707.2019.8958662

M. Tropea, G. Fedele

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引用次数: 11

摘要

本文介绍了卷积神经网络(CNN)中用于图像分类的五种不同分类器(多类逻辑回归(MLR)、支持向量机(SVM)、k-近邻(kNN)、随机森林(RF)和高斯朴素贝叶斯(GNB))的比较。在我们的实验中，我们使用了一个数据集，该数据集由属于256个不同类别的物体图像组成，称为Caltech 256。

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Classifiers Comparison for Convolutional Neural Networks (CNNs) in Image Classification

This paper presents a comparison between five different classifiers (Multi-class Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB)) to be used in a Convolutional Neural Network (CNN) in order to perform images classification. For our experiments we have used a dataset composed of images of objects belonging to 256 widely varied categories called Caltech 256.

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2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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