Open AccessExploration of machine algorithms based on deep learning model and feature extraction
Author(s) -
Yufeng Qian
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021376
Subject(s) - artificial intelligence , machine learning , computer science , lifelong learning , algorithm , instance based learning , wake sleep algorithm , unsupervised learning , semi supervised learning , deep learning , population based incremental learning , feature (linguistics) , active learning (machine learning) , online machine learning , genetic algorithm , psychology , pedagogy , linguistics , philosophy , generalization error
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.