Special issue on extreme learning machine and deep learning networks
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EDITORIAL
Special issue on extreme learning machine and deep learning networks Zhihong Man1 • Guang-Bin Huang2
Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Welcome to this special issue of neural computing and applications on extreme learning machine (ELM) and deep learning networks (DLN). Since 1990s, ELM has been becoming a very important learning methodology for neural networks [1–3]. ELM has integrated both machine learning and biological learning mechanisms to train neural networks to perform various tasks including pattern classification, decision making and system modelling in science and engineering [4–6]. In recent years, the biological learning features of ELM have stimulated the researcher and engineers to combine ELM with many other learning structures such as DLN and Bayseian networks to perform complex big data processing in many areas [7–10]. Viewing the rapid development of ELM theory and applications, we planned to organize this special issue a year ago for the readers and neural computing society to report the new ideas and innovations in both ELM and DLN areas. After rigorously reviewing all of received 60 papers on the basis of innovativeness and relevance for all NCA readers, we finally selected 21 high-quality papers for this special issue. The following is the brief introduction of these articles in this special issue. In ‘‘A new intelligent pattern classifier based on deepthinking’’, the authors propose a brain-like intelligent pattern classifier, aiming at using the human being’s thinking logics and experience to design the pattern classifiers and avoid the matrix inverse computation in conventional classifier designs. It is seen that the proposed classifier has no parameters to be determined via mathematical optimization. Instead, it is designed by using the correlation principles to construct the pattern clusters at first. The middle-level feature vectors can then be extracted
& Zhihong Man [email protected] 1
Swinburne University of Technology, Melbourne, Australia
2
Nanyang Technological University, Singapore, Singapore
from the statistical information of the correlation information between the input data vectors and the ones stored in each pattern cluster. For accurate classification purpose, the advanced feature vectors are generated with the moments’ information of the middle-level feature vectors. After that, Bayesian inference is implemented for decision making from the weighted sum of the advanced feature components. In addition, a realtime fine-tuning loop (layer) is designed to adaptively ‘‘widen’’ the border of each pattern clustering region such that the input data vectors can be directly classified once they are located in one of the clustering regions. An experiment for the classification of the handwritten digit images from the MNIST database is performed to show the excellent performance and effectiveness of the proposed brain-like pattern classifier. In ‘‘Inverse partitioned matrix-based semi-random incre
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