A Novel Automatic Neural Network Feature Extractor

一种新型自动神经网络特征提取器

• 批准号: DP210100640
• 负责人: Prof Brijesh Verma
• 金额: $ 29.95万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210100640
• 关键词: Novel; Automatic; Neural; Network; Feature

This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.

该项目旨在研究卷积神经网络和传统神经网络的特征提取能力，并开发一种通用的特征抽取器，可以适用于各种真实世界的图像和非图像数据。将介绍和评估自动特征提取、特征解释、混合进化算法和非迭代集成学习的新概念。预期的结果是一个用于自动提取特征的通用特征抽取器，一个用于寻找最佳参数的优化器，以及用于将特征分类为类的非迭代集成学习技术。该项目的影响将是用于实际应用的自动特征提取和分类器。