基于深度学习的特征提取:综述

Suresh Dara, Priyanka Tumma
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引用次数: 84

摘要

深度学习是当前机器学习技术和模式分类关联的一个有效研究领域。这在计算机视觉、语音识别和自然语言处理等应用领域取得了巨大的成功。本文给出了卷积神经网络(CNN)等深度学习技术中特征提取的影响。本文的目的是对近五年来有关特征提取方法的实际文献进行综述。随着应用需求的增加,特征提取领域的大量研究和分析变得非常高效。在本文中,我们对深度学习进行了详细的研究,并描述了一些现有的特征提取方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Extraction By Using Deep Learning: A Survey
Deep learning is presently an effective research area in machine learning technique and pattern classification association. This has achieved big success in the areas of application namely computer vision, speech recognition, and NLP. This paper gives the impact of feature extraction that used in a deep learning technique such as Convolutional Neural Network (CNN). The purpose of this paper presents an emerged survey of actual literatures on feature extraction methods since past five years. As the raising of application demand increases, a large study and analysis in the feature extraction field became very efficient. In this paper, we presented a detailed study on deep learning, and a described some of existing methodology of feature extraction.
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