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15 Aug 2021
Abstract:
This talk will first introduce the main randomization-based feedforward neural networks. The popular instantiation of the feedforward type called random vector functional link neural network (RVFL) originated in early 1990s. Other feedforward methods are random weight neural networks (RWNN), extreme learning machines (ELM), Broad Learning System, Stochastic Configuration Network, etc. Another paradigm is based on kernel trick such as the kernel ridge regression which includes randomization for scaling to large training data. The talk will also consider computational complexity with increasing scale of the classification problems. Another randomization-based paradigm is the random forest which exhibits highly competitive performances. The talk will also present extensive benchmarking studies using classification datasets.
Biography:
Dr P. N. Suganthan received the B.A degree and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990 and 1994, respectively. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. After completing his PhD research in 1995, he served as a predoctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 199596 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 199699. He moved to Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013?2018). He is/was an associate editor of the Applied Soft Computing (Elsevier, 2018?), Neurocomputing (Elsevier, 2018?), IEEE Trans on Cybernetics (2012 ? 2018), IEEE Trans on Evolutionary Computation (2005 ? ), Information Sciences (Elsevier) (2009 ? ), Pattern Recognition (Elsevier) (2001 ? ) and IEEE Trans on SMC: Systems (2020 ? ) Journals. He is a founding co?editor?in?chief of Swarm and Evolutionary Computation (2010 ? ), an SCI Indexed Elsevier Journal.
This talk will first introduce the main randomization-based feedforward neural networks. The popular instantiation of the feedforward type called random vector functional link neural network (RVFL) originated in early 1990s. Other feedforward methods are random weight neural networks (RWNN), extreme learning machines (ELM), Broad Learning System, Stochastic Configuration Network, etc. Another paradigm is based on kernel trick such as the kernel ridge regression which includes randomization for scaling to large training data. The talk will also consider computational complexity with increasing scale of the classification problems. Another randomization-based paradigm is the random forest which exhibits highly competitive performances. The talk will also present extensive benchmarking studies using classification datasets.
Biography:
Dr P. N. Suganthan received the B.A degree and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990 and 1994, respectively. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. After completing his PhD research in 1995, he served as a predoctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 199596 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 199699. He moved to Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013?2018). He is/was an associate editor of the Applied Soft Computing (Elsevier, 2018?), Neurocomputing (Elsevier, 2018?), IEEE Trans on Cybernetics (2012 ? 2018), IEEE Trans on Evolutionary Computation (2005 ? ), Information Sciences (Elsevier) (2009 ? ), Pattern Recognition (Elsevier) (2001 ? ) and IEEE Trans on SMC: Systems (2020 ? ) Journals. He is a founding co?editor?in?chief of Swarm and Evolutionary Computation (2010 ? ), an SCI Indexed Elsevier Journal.
