Description
The concept of deep machine learning is easier to understand by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the cycle-to-cycle variations.
Author: Arash Gharehbaghi
Publisher: CRC Press
Published: 07/07/2023
Pages: 196
Binding Type: Hardcover
Weight: 1.00lbs
Size: 9.37h x 6.14w x 0.63d
ISBN13: 9780367321789
ISBN10: 0367321785
BISAC Categories:
- Mathematics | Probability & Statistics | Time Series
- Computers | Data Science | Data Modeling & Design
- Science | Life Sciences | General
About the Author
Arash Gharehbaghi obtained a M.Sc. degree in biomedical engineering from Amir Kabir University, Tehran, Iran, in 2000, an advanced M.Sc. of Telemedia from Mons University, Belgium, and PhD degree of biomedical engineering from Linköping University, Sweden in 2014. He is a researcher at the School of Information Technology, Halmstad University, Sweden. He has conducted several studies on signal processing, machine learning and artificial intelligence over two decades that led to the international patents, and publications in high prestigious scientific journals.
He has proposed new learning methods for learning and validating time series analysis, among which Time-Growing Neural Network, and A-Test are two recent ones that have interested the machine learning community. He won the first prize of young investigator award from the International Federation of Biomedical Engineering in 2014.
This title is not returnable