王殿辉教授1995年3月获东北大学工业自动化专业博士学位，1995-1997在新加坡南洋理工大学电子工程学院做博士后研究工作，1998-2001在香港理工大学计算学系研究员，从事机器学习，数据挖掘和图像处理方面的研究工作。2001年7月至今在澳大利亚La Trobe大学计算机科学与信息技术系从事教学与科研工作。主要研究方向：计算智能与数据挖掘技术在大数据信息处理和智能系统方面的应用研究， 发表研究论文200余篇。目前是IEEE高级会员，博士生导师，任《International Journal of Machine Intelligence and Sensory Signal Processing》主编，《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Cyebernetics》、《Information Sciences》、《 Neurocomputing》等多个国际期刊的副主编。
Dr Wang received his PhD degree in March 1995, from the School of Information Science and Engineering, Northeastern University, Shenyang, China. From September 1995 to August 1997, he worked as a Postdoctoral Fellow at the School of Electronic and Electrical Engineering, Nanyang Technological University, Singapore. He then worked as a Research Associate and Research Fellow for three years until the end of June 2001 in the Department of Computing, The Hong Kong Polytechnic University, Hong Kong. Since July 2001, he has been with the Department of Computer Science and Computer Engineering at La Trobe University, Australia, and currently working in the same department as a Reader and Associate Professor. Since 2010, Dr Wang has been an adjunct Professor in The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, China.
He is a Senior Member of IEEE, and serving as an Editor-in-Chief for the Int. J. of Machine Intelligence and Sensory Signal Processing, Associate Editors for several international journals including IEEE Trans. On Neural Networks and Learning Systems, IEEE Trans. On Cybernetics, INFORMATION SCIENCE, NEUROCOMPUTING, and a Subject Editor for APPLIED MATHEMATICAL MODELLING.
Dr. Wang's working areas include machine learning, data mining and computational intelligence systems for Bioinformatics and Engineering Applications. Technically, his research focus falls in subtle pattern discovery and recognition using neural networks and fuzzy systems, and in recent years he has been working towards to the development of randomized methods for neural networks, specifically, contributing to develop a brand new framework for building randomized learner models, termed as Stochatic Configuration Networks (SCNs). In contrast to the existing randomised learning algorithms for single layer feed-forward neural networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either constructive or selective manner. As fundamentals of SCN-based data modelling techniques, we establish some theoretical results on the universal approximation property. Some experimental results indicate that our proposed SCNs outperform than other randomized neural networks in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning and sound generalization. Deep sctochastic configuration networks (DeepSCNs) have been developed and mathematically proved as universal approximators for continous nonlinear functions defined over compact sets. DeepSCNs can be constructed efficiently (much faster than other deep neural networks) and share many great features, such as learning representation and consistency property between learning and generalization. Details about SCNs and DeepSCNs could be found in our relevant publications at this homepage or ResearchGate.
Students with sound mathematics knowledge and/or strong computing backgrounds are warmly welcome to work with me for a higher degree (PhD or Research Masters). For applicants who wish to apply for a scholarship from La Trobe University, you may get more detailed information from the link of Scholarship. Further information on research topics can be found at our Intelligent Search and Discovery (ISD) Laboratory page.
D. H. Wang and M. Li, Deep stochastic cofiguration networks: Universal approximation and learning representation, arXiv:1702.05639 [cs.LG], Feb. 18, 2017.
D. H. Wang and M. Li, Stochastic configuration networks: Fundamentals and algorithms, arXiv:1702.03180v3 [cs.NE], Feb. 10, 2017.
S. Scardapane and D. H. Wang, Randomness in neural networks: An overview, WIREs Data Mining Knowledge Discovery, 2017, 7:e1200. doi: 10.1002/widm.1200.
D. H. Wang, Editorial: Randomized algorithms for training neural networks, Information. Sciences, Vol. 364-365, No. 10, pp. 126-128, 2016.
Y. Xu, R. Zhang, J. H. Zhao, Z. Y. Dong, D. H. Wang, H. Yang and K. P. Wong, Assessing short-term voltage stability of power systems in real-timeusing a hybrid intelligent system, IEEE Transactions On Neural Networks and Learning Systems, Vol. 27, No.8, pp. 1686-1696, Aug. 2016.
S. Tapan and D. H. Wang, A further study on mining DNA motifs using fuzzy self-organizing maps, IEEE Transactions On Neural Networks and Learning Systems, Vol. 27, No. 1, pp. 113-124, Jan. 2016.
W. T. Li, D. H. Wang and T. Y. Chai, Multi-source data ensemblemodeling for clinker free lime content in rotary kiln sintering processes, IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 45, No. 2, pp. 303-314, Feb. 2015.
M. Alhamdoosh and D. H. Wang, Fast decorrelated neural network ensembles with random weights, Information Sciences, Vol. 264, pp. 104-117, 2014. ( MATLAB codes can be downloaded here)
D. H. Wang and S. Tapan, A robust elicitation algorithm for discovering DNA motifs using fuzzy self-organizing maps, IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 10, pp. 1677-1688, October 2013.
Z. H. Man, K. Lee, D. H. Wang, Z. W. Cao, and S. Y. Khoo, A robust single-hidden layer feedforward network based pattern classifier, IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 12, pp. 1974-1986, December, 2012.
D. H. Wang and S. Tapan, MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences, BMC Systems Biology, 6(Suppl 2):S4, December 2012.
W. T. Li, D. H. Wang and T. Y. Chai, Flame image-based burning state recognition for sintering process of rotary kiln using heterogeneous features and fuzzy integral, IEEE Transactions on Industrial Informatics, Vol. 8, No. 4, pp. 780-790, November 2012.
Y. F. Wang, D. H. Wang, and T. Y. Chai, Extraction and adaptation of fuzzy rules for friction modeling and control compensation, IEEE Transactions on Fuzzy Systems, Vol. 19, No. 4, pp. 682-693, Aug. 2011.
N. K. Lee and D. H. Wang, SOMEA: self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model, BMC Bioinformatics. 2011; 12(Suppl 1): S16.
K. Meng, Z. Y. Dong, D. H. Wang, and K. P. Wong, Self-adaptive RBF neural network classifier for transformer fault analysis, IEEE Transactions on Power Systems, Vol. 25, No.3, pp. 1350-1360, Aug. 2010.
R. Yu and D. H. Wang, On impulse mode of linear singular systems subject to decentralized output feedback, IEEE Transactions on Automatic Control, Vol. 48, No.10, pp.1804-1809, 2003.
R. Yu and D. H. Wang, Structural properties and poles assignability of LTI singular systems under output feedback, Automatica, Vol.39, pp.685-692, April, 2003.
R. Yu and D. H. Wang, Algebraic properties of singular systems subject to decentralized output feedback, IEEE Transactions on Automatic Control, Vol. 47, No.11, pp. 1898 -1903, 2002.
D. H. Wang and P. Bao, Robust impulse control of uncertain singular systems by decentralized output feedback, IEEE Transactions on Automatic Control, Vol.45, No.3, pp.500-505, 2000.
D. H. Wang and C. B. Soh, On regularizing singular systems by decentralized output feedback, IEEE Transactions on Automatic Control, Vol.44, No.1, pp.148-152, 1999.
D. H. Wang and X. Li, iGAPK: improved GAPK algorithm for regulatory DNA motif discovery, 17th International Conference on Neural Information Processing (ICONIP10), November 22-25, 2010, Sydney, Australia.
D. H. Wang and S. Tapan, Fuzzy filtering systems for performing environment improvement of computational DNA motif discovery, Proceedings of IEEE-FUZZ, July 18-23, 2010, Barcelona, Spain.
X. Li and D. H. Wang, Computational discovery of regulatory DNA motifs using evolutionary computation, IEEE Congress on Evolutionary Computation (CEC10), July 18-23, 2010, Barcelona, Spain.
H. T. Do and D. H. Wang, Overlap-based similarity metrics for motif search in DNA sequences, 16th International Conference on Neural Information Processing (ICONIP09), pp. 465-474, , December 1-5, 2009, Bangkok, Thailand.
D. H. Wang and X. Li, GAPK: Genetic algorithms with prior knowledge for motif discovery in DNA sequences, IEEE Congress on Evolutionary Computation (CEC09), pp. 277-284, May 18-21, Trondheim, Norway.
D. H. Wang and N. K. Lee, Computational discovery of motifs using hierarchical clustering techniques, IEEE International Conference on Data Mining (ICDM08), pp. 1073-1078, December 15-19, 2008, Pisa, Italy.
D. H. Wang and N. K. Lee, MISCORE: mismatch-based matrix similarity scores for DNA motifs detection, 15th International Conference on Neural Information Processing (ICONIP08), pp. 478-485, November 25-28, 2008, Auckland, New Zealand.
P. C. Conilione and D. H. Wang, Intelligent face image retrieval using eigenpaxels and learning similarity metrics, 15th International Conference on Neural Information Processing (ICONIP08), pp. 792-799, November 25-28, 2008, Auckland, New Zealand.
D. H. Wang, Modeling performance enhancement with constrained linear filters, IEEE International Joint Conference on Neural Networks (IJCNN08), pp. 699-703, June 1-6, 2008, Hong Kong, China.
D. H. Wang, B-MISCORE: a new similarity metric for self-organization of DNA k-mers, Technical Report LTU-22-06-2013, June 22, 2013, La Trobe University, Australia.
Unit Name: Data Mining (CSE4DMI)
Data mining refers to various techniques which can be used to uncover hidden information from a database. The data to be mined may be complex data including multimedia, spatial and temporal, and biological data. Data mining has evolved from several areas including: databases, artificial intelligence, algorithms, information retrieval and statistics. This unit is designed to provide graduate students with a solid understanding of data mining concepts and techniques. The unit covers rule-based and learning-based data classification, clustering algorithms and association rule mining techniques. As domain applications of data mining techniques in Bioinformatics, classification or clustering tasks will be covered in assignment for this unit.
Unit Name: Computational Intelligence (CSE3CI)
Quantitive analysis plays an important role in business analytics and knowledge engineering, thus it is very useful to develop computing skills for data regression and classification. This subject covers some fundamentals of computational intelligence techniques, including fuzzy inference systems, neural networks and hybrid neuro-fuzzy systems. The subject is designed with a focus on solving time-series forecasting problems using fuzzy inference systems, where fuzzy inference mechanisms and fuzzy rule extraction from numerical data are addressed. Some advanced learning techniques for training neural networks will also be highlighted. In labs and assignment students will work with business datasets for time-series prediction using a fuzzy system, which helps to consolidate the knowledge taught in the lectures and gain a hand-on experience on computational intelligence applications in business.
Unit Name: Visual Information Systems (CSE3VIS)
This subject covers an overview of visual information systems, visual data srepresentation, feature extraction, image processing, content-based image retrieval techniques and fundamentals of image recognition and classification techniques. Design issues on content-based image retrieval systems for image database management and/or image data mining for engineering applications will be addressed, which contain image feature extraction, indexing, similarity measure, lower-bounding lemma, classificantion systems design and performance evaluation. Practice on design of content-based image retrieval systems will be offered in Labs. Knowledge on HTML, PHP and MySQL, will be needed to implement content-based image retrieval systems
Objectives: Our major areas of interest are searching and discovering subtle patterns from sequence data, with applications for bioinformatics, business analytics and engineering. The ISD lab at La Trobe University foucuses on (1) developing advanced computational approaches and techniques to offer better solutions on robust discovery of subtle patterns for time serieses and large scale DNA datasets using data mining and computational intelligence techniques; and (2) exploring closed-loop learning algorithms with user's relevance feedback for big data regression and classification.
Subjects Taught at La Trobe University
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