Introduction to signal processing and machine learning theory

OLIVEIRA KUHFUSS DE MENDONÇA, Marcele; Apolinário, Isabela F.; Diniz, Paulo S.R.

doi:10.1016/B978-0-32-391772-8.00007-7

No full text

Contribution to collective works (Parts of books)

Introduction to signal processing and machine learning theory

OLIVEIRA KUHFUSS DE MENDONÇA, Marcele; Apolinário, Isabela F.; Diniz, Paulo S.R.

2023 • In Signal Processing and Machine Learning Theory

Peer reviewed

Permalink
https://hdl.handle.net/10993/63461

DOI
10.1016/B978-0-32-391772-8.00007-7

Files (0)Send to Details Statistics Bibliography Similar publications

Files

Full Text

No document available.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

adaptive filters; continuous-time signals and systems; data representation: from multiscale transforms to neural networks; dictionaries in machine learning; digital filter structures and their implementation; discrete-time signals and systems; frames in signal processing; machine learning: review and trends; modern transform design for practical audio/image/video coding applications; multirate signal processing for software radio architectures; nonconvex graph learning: sparsity, heavy tails, and clustering; parametric estimation; random signals and stochastic processes; sampling and quantization; signal processing over graphs; tensor methods in deep learning; Engineering (all); Computer Science (all)

Abstract :

[en] Signal processing and machine learning theories are critical enablers for implementing many amazingly sophisticated technological advances. In this chapter we attempt to provide a brief introduction to some key topics in these dynamic areas, which are further discussed in this book. Together, they create a rich toolbox to pave the way for future societal developments.

Disciplines :

Electrical & electronics engineering

Author, co-author :

OLIVEIRA KUHFUSS DE MENDONÇA, Marcele ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

Apolinário, Isabela F.; Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

Diniz, Paulo S.R.; Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

External co-authors :

yes

Language :

English

Title :

Introduction to signal processing and machine learning theory

Publication date :

2023

Main work title :

Signal Processing and Machine Learning Theory

Publisher :

Elsevier

ISBN/EAN :

978-0-323-91772-8
978-0-323-97225-3

Peer reviewed :

Peer reviewed

Additional URL :

https://api.elsevier.com/content/article/PII:B9780323917728000077?httpAccept=text/xml

Available on ORBilu :

since 06 January 2025

Statistics

Number of views

81 (0 by Unilu)

Number of downloads

0 (0 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

A. Antoniou and W.-S. Lu. Practical Optimization: Algorithms and Engineering Applications. New York, NY, 2nd Edition: Springer, 2021.
Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin. Learning from Data. Pasadena, CA: AMLbook.com, 2012.
A. Antoniou. “On the roots of digital signal processing: Part I and II”. IEEE Circuits and Systems Magazine 7 (2007), No 1: 8-18 and No 4: 8-19.
A. Antoniou. Digital Filters: Analysis, Design, and Signal Processing Applications. New York, NY: McGraw-Hill, 2018.
C. M. Bishop. Pattern Recognition and Machine Learning. New York, NY: Springer, 2006.
R. E. Crochiere and L. R. Rabiner. Multirate Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1983.
P. S. R. Diniz, M. L. R. de Campos, et al. Online Learning and Adaptive Filters. Cambridge, UK: Cambridge University Press, 2022.
P. S. R. Diniz. Adaptive Filtering: Algorithms and Practical Implementation. New York, NY, 5th Edition: Springer, 2020.
P. S. R. Diniz, W. A. Martins, and M. V. S. Lima. Block Transceivers: OFDM and Beyond. New York, NY: Springer, 2012.
P. S. R. Diniz, E. A. Silva, and S. L. Netto. Digital Signal Processing: System Analysis and Design. Cambridge, UK, 2nd Edition: Cambridge University Press, 2010.
R. Fletcher. Practical Methods of Optimization. Chichester, UK: John Wiley & Sons., 1980.
I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. Cambridge, MA: MIT Press, 2016.
S. Haykin. Neural Networks and Learning Machines. Englewood Cliffs, NJ, 3rd Edition: Prentice-Hall, 2008.
S. Haykin. Adaptive Filter Theory. Englewood Cliffs, NJ, 5th Edition: Prentice-Hall, 2014.
A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. New York, NY: Wiley Interscience, 2001.
T. Hastie, R. Tibshirani, and J. Friedman. Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer, 2016.
S. M. Kay. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.
D. G. Luenberger. Introduction to Linear and Nonlinear Programming. Boston, MA, 2nd Edition: Addison-Wesley, 1984.
S. Mallat. A Wavelet Tour of Signal Processing: The Sparse Way. Burlington, MA, 3rd Edition: Academic Press, 2009.
S. K. Mitra. Digital Signal Processing: A Computer-Based Approach. New York, NY, 4th Edition: McGraw-Hill, 2010.
K. P. Murphy. Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012.
A. V. Oppenheim and R. W. Schaffer. Discrete-Time Signal Processing. Englewood Cliffs, NJ, 3rd Edition: Prentice-Hall, 2010.
A. V. Oppenheim, A. S. Willsky, and A. H. Nawab. Signals and Systems. Englewood Cliffs, NJ, 2nd Edition: Prentice-Hall, 1997.
J. G. Proakis and D. G. Manolakis. Digital Signal Processing: Principles, Algorithms, and Applications. Englewood Cliffs, NJ, 4th Edition: Prentice-Hall, 2006.
J. G. Proakis and D. G. Manolakis. Applied Digital Signal Processing. Cambridge, UK: Cambridge University Press, 2011.
A. Papoulis and S. UnniKrishna Pillai. Probability, Random Variables, and Stochastic Processes. New York, NY, 4th Edition: McGraw-Hill, 2002.
A. H. Sayed. Adaptive Filters. Hoboken, NJ: John Wiley & Sons, 2008.
J. J. Shynk. Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications. Hoboken, NJ: John Wiley & Sons, 2013.
J. B. O. Souza Filho et al. Online Component Analysis, Architectures and Applications. Foundations and Trends in Signal Processing 16 (3-4) (2022), pp 224-429. DOI:10.1561/2000000112; Boston, MA: NOW Publishers
B. Schölkopf and A. J. Smola. Learning with Kernels: State Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2002.
S. Theodoridis. Machine Learning: A Bayesian and Optimization Perspective. London, UK, 2nd Edition: Academic Press, 2020.
D. Tse and P. Viswanath. Fundamentals of Wireless Communication. Cambridge, UK: Cambridge University Press, 2005.
P. P. Vaidyanathan. Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, 1993.
H. L. Van Trees. Optimum Array Processing. New York, NY: John Wiley & Sons, 2002.
V. N. Vapnik. Statistical Learning Theory. New Delhi, India: Wiley Interscience, 1998.
Vetterli, M. and Kovačević, J. Wavelets and Subband Coding. Englewood Cliffs, NJ: Prentice-Hall, 1995.
P. P. Vaidyanathan, S.-M. Phoong, and Y.-P. Lin. Signal Processing and Optimization for Transceiver Systems. Cambridge, UK: Cambridge University Press, 2010.