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Open AccessArticle

Lifelong Machine Learning Architecture for Classification

Research Institute of Big Data Analytics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Department of Computer Science, The University of Liverpool, Liverpool L69 3BX, UK
Author to whom correspondence should be addressed.
Symmetry 2020, 12(5), 852;
Received: 11 March 2020 / Revised: 23 April 2020 / Accepted: 5 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
Benefiting from the rapid development of big data and high-performance computing, more data is available and more tasks could be solved by machine learning now. Even so, it is still difficult to maximum the power of big data due to each dataset is isolated with others. Although open source datasets are available, algorithms’ performance is asymmetric with the data volume. Hence, the AI community wishes to raise a symmetric continuous learning architecture which can automatically learn and adapt to different tasks. Such a learning architecture also is commonly called as lifelong machine learning (LML). This learning paradigm could manage the learning process and accumulate meta-knowledge by itself during learning different tasks. The meta-knowledge is shared among all tasks symmetrically to help them to improve performance. With the growth of meta-knowledge, the performance of each task is expected to be better and better. In order to demonstrate the application of lifelong machine learning, this paper proposed a novel and symmetric lifelong learning approach for sentiment classification as an example to show how it adapts different domains and keeps efficiency meanwhile. View Full-Text
Keywords: lifelong machine learning; continuous learning; sentiment classification; natural language processing lifelong machine learning; continuous learning; sentiment classification; natural language processing
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Hong, X.; Guan, S.-U.; Man, K.L.; Wong, P.W.H. Lifelong Machine Learning Architecture for Classification. Symmetry 2020, 12, 852.

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