| Description |
1 online resource. |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
"The concept of deep machine learning becomes easier to understandable 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 beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level"-- Provided by publisher. |
| Biography |
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 Linkp̲ing 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. |
| Subject |
Time-series analysis -- Data processing.
|
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Deep learning (Machine learning)
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Série chronologique -- Informatique. |
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Apprentissage profond. |
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Deep learning (Machine learning) |
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Time-series analysis -- Data processing |
| Other Form: |
Print version: Gharehbaghi, Arash, 1972- Deep learning in time series analysis. First edition. Boca Raton : CRC Press, 2023 9780367321789 (DLC) 2022045955 (OCoLC)1358404799 |
| ISBN |
9780429321252 (electronic bk.) |
|
0429321252 (electronic bk.) |
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9781000911435 (electronic bk. : EPUB) |
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1000911438 (electronic bk. : EPUB) |
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9781000911404 (electronic bk. : PDF) |
|
1000911403 (electronic bk. : PDF) |
| Standard No. |
10.1201/9780429321252 doi |
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