本文针对无人机LiDAR点云地物分类问题，提出了一种基于改进的卷积神经网络模型，通过对四川某地区采集的点云数据进行人工标注，实现了地面、建筑和植被三种地物的点云分类。利用所设计的CNN模型对人工构建的数据集进行模型训练及测试，并不断对模型参数进行优化，总体分类精度 (OA)可达93.6599%。实验结果表明，本文所设计的改进CNN能够自动学习点云的空间分布特征和形状模式，减少了人工特征设计的工作量 ...

Dr. James McCaffrey of Microsoft Research provides a full-code, step-by-step machine learning tutorial on how to use the LightGBM system to perform multi-class classification using Python and the ...

This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification. Methods: To address the absence of a RATD image dataset, we crowdsourced 900 ...

MCNN-LSTM: Combining CNN and LSTM to Classify Multi-Class Text in Imbalanced News Data Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient ...

This repository contains code and resources for performing multi-class classification on the CIFAR-10 dataset using transfer learning. Transfer learning is a powerful technique that leverages the ...

The experimental results demonstrate that the classification results of both centralized multi-person data fusion CNNs outperform the CNN classification results in single-person mode, and the four ...

Hence the team has devised a new method that combines classification problem data with multiple labels with the capacity to learn new things from data over time. The proposed method outperformed ...

Multi-class classification pipeline to predict earthquake damage as part of the Data Science Lab 2023 at KIT (Karlsruher Institute of Technology, Phase I. We won the internal course competition with ...

Dr. James McCaffrey of Microsoft Research: When multi-class data is skewed toward one or more classes, it's very important to analyze accuracy by class.