Exploration of machine algorithms based on deep learning model and feature extraction

The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with a deep learning algorithm. First, based on the the...

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Autor principal:	Yufeng Qian
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	deep learning model feature extraction machine learning genetic algorithms mutual information Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/bd7b01391f984eda8f52fa79835a1a53
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spelling	oai:doaj.org-article:bd7b01391f984eda8f52fa79835a1a532021-11-23T02:24:50ZExploration of machine algorithms based on deep learning model and feature extraction10.3934/mbe.20213761551-0018https://doaj.org/article/bd7b01391f984eda8f52fa79835a1a532021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021376?viewType=HTMLhttps://doaj.org/toc/1551-0018The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with a deep learning algorithm. First, based on the theoretical basis of the deep learning model and feature extraction. The study analyzes several representative machine learning algorithms, and compares the performance of the optimized deep learning model with other algorithms in a practical application. By explaining the machine learning system, the study introduces two typical algorithms in machine learning, namely ELLA (Efficient lifelong learning algorithm) and HLLA (Hierarchical lifelong learning algorithm). Second, the flow of the genetic algorithm is described, and combined with mutual information feature extraction in a machine algorithm, to form a composite algorithm HLLA (Hierarchical lifelong learning algorithm). Finally, the deep learning model is optimized and a deep learning model based on the HLLA algorithm is constructed. When K = 1200, the classification error rate reaches 0.63%, which reflects the excellent performance of the unsupervised database algorithm based on this model. Adding the feature model to the updating iteration process of lifelong learning deepens the knowledge base ability of lifelong machine learning, which is of great value to reduce the number of labels required for subsequent model learning and improve the efficiency of lifelong learning.Yufeng QianAIMS Pressarticledeep learning modelfeature extractionmachine learninggenetic algorithmsmutual informationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7602-7618 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning model feature extraction machine learning genetic algorithms mutual information Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	deep learning model feature extraction machine learning genetic algorithms mutual information Biotechnology TP248.13-248.65 Mathematics QA1-939 Yufeng Qian Exploration of machine algorithms based on deep learning model and feature extraction
description	The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with a deep learning algorithm. First, based on the theoretical basis of the deep learning model and feature extraction. The study analyzes several representative machine learning algorithms, and compares the performance of the optimized deep learning model with other algorithms in a practical application. By explaining the machine learning system, the study introduces two typical algorithms in machine learning, namely ELLA (Efficient lifelong learning algorithm) and HLLA (Hierarchical lifelong learning algorithm). Second, the flow of the genetic algorithm is described, and combined with mutual information feature extraction in a machine algorithm, to form a composite algorithm HLLA (Hierarchical lifelong learning algorithm). Finally, the deep learning model is optimized and a deep learning model based on the HLLA algorithm is constructed. When K = 1200, the classification error rate reaches 0.63%, which reflects the excellent performance of the unsupervised database algorithm based on this model. Adding the feature model to the updating iteration process of lifelong learning deepens the knowledge base ability of lifelong machine learning, which is of great value to reduce the number of labels required for subsequent model learning and improve the efficiency of lifelong learning.
format	article
author	Yufeng Qian
author_facet	Yufeng Qian
author_sort	Yufeng Qian
title	Exploration of machine algorithms based on deep learning model and feature extraction
title_short	Exploration of machine algorithms based on deep learning model and feature extraction
title_full	Exploration of machine algorithms based on deep learning model and feature extraction
title_fullStr	Exploration of machine algorithms based on deep learning model and feature extraction
title_full_unstemmed	Exploration of machine algorithms based on deep learning model and feature extraction
title_sort	exploration of machine algorithms based on deep learning model and feature extraction
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/bd7b01391f984eda8f52fa79835a1a53
work_keys_str_mv	AT yufengqian explorationofmachinealgorithmsbasedondeeplearningmodelandfeatureextraction
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Exploration of machine algorithms based on deep learning model and feature extraction

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