Paulo S. R. Diniz

Plenary Speech I

Paulo S. R. Diniz
COPPE/Poli, Federal University of Rio de Janeiro,
Program of Electrical Engineering and Department of Electrical and Computer Engineering,
Rio de Janeiro, R.J., Brazil

Speaker bio:

Paulo S. R. Diniz was born in Niterói, Brazil.. He received the Electronics Eng. degree (Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree from COPPE/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P.Q., Canada, in 1984, all in electrical engineering. Since 1979 he has been with the Department of Electronics and Computer Engineering UFRJ. He has also been with the Program of Electrical Engineering (the graduate studies dept.), COPPE/UFRJ, since 1984, where he is presently a Professor. He held temporary positions at the Department of Electrical and Computer Engineering of University of Victoria, Victoria, B.C., Canada; Helsinki University of Technology (now Aalto University); and in the Department of Electrical Engineering of University of Notre Dame, Notre Dame, IN, USA. His teaching and research interests are in analog and digital signal processing, adaptive signal processing, learning from data, digital communications, wireless communications, multi-rate systems, stochastic processes, and electronic circuits.

He has published over 100 refereed papers in journals and over 200 conference papers in some of these areas, and wrote the text books ADAPTIVE FILTERING: Algorithms and Practical Implementation, Fifth Edition, Springer, NY, 2020, and DIGITAL SIGNAL PROCESSING: System Analysis and Design, Second Edition, Cambridge University Press, Cambridge, UK, 2010 (with E. A. B. da Silva and S. L. Netto), and the monograph BLOCK TRANSCEIVERS: OFDM and Beyond, Morgan & Claypool, New York, NY, 2012 (W. A. Martins, and M. V. S. Lima).

He was a distinguished lecturer of the IEEE Circuits and Systems Society, and of the IEEE Signal Processing Society. He also received the 2004 Education Award and the 2014 Charles Desoer Technical Achievement Award both from IEEE Circuits and Systems Society. He also holds some best-paper awards from conferences and an IEEE journal. Prof. P. S. R. Diniz is a member of the National Academy of Engineering (ANE), and of the Brazilian Academy of Science (ABC). He is a Fellow of IEEE and EURASIP.

Title: Data-Selection in Machine Learning Algorithms

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifies to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some adaptive filtering and machine learning algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data or some simple estimation based on the available data.

Test results show the effectiveness of the proposed algorithms for selecting the innovative data without sacrificing the estimation accuracy, while reducing the computational cost. In neural networks applications the strategy leads to reduced training errors and improved test results.

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  • Important Dates

    • Paper Submission Deadline:
    • September 1, 2019
    • September 15, 2019
    • Notification of Acceptance:
    • September 20, 2019
    • Registration Deadline:
    • October 10, 2019
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  • Sponsors

    •  Dalian Jiaotong University
    •  IEEE Harbin Section


    • Research Center for Medical and Healthcare Management, Ritsumeikan University, JAPAN
    • Shenyang University of Technology
    • Liaoning Province Software Industry School-Enterprise Alliance