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Source separation and machine learning /
Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approach...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press, an imprint of Elsevier,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
| 005 | 20231120010320.0 | ||
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| 008 | 181023t20192019enk ob 001 0 eng d | ||
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| 019 | |a 1105189200 |a 1105565274 | ||
| 020 | |a 9780128045770 |q (electronic bk.) | ||
| 020 | |a 0128045779 |q (electronic bk.) | ||
| 020 | |a 9780128177969 |q (electronic bk.) | ||
| 020 | |a 0128177969 |q (electronic bk.) | ||
| 020 | |z 9780128045664 | ||
| 035 | |a (OCoLC)1057550047 |z (OCoLC)1105189200 |z (OCoLC)1105565274 | ||
| 050 | 4 | |a TK5102.9 |b .C45 2019 | |
| 072 | 7 | |a TEC |x 009070 |2 bisacsh | |
| 082 | 0 | 4 | |a 621.3822 |2 23 |
| 100 | 1 | |a Chien, Jen-Tzung, |e author. | |
| 245 | 1 | 0 | |a Source separation and machine learning / |c Jen-Tzung Chien. |
| 264 | 1 | |a London : |b Academic Press, an imprint of Elsevier, |c [2019] | |
| 264 | 4 | |c �2019 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 24, 2018). | |
| 520 | |a Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. | ||
| 650 | 0 | |a Blind source separation. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a S�eparation aveugle de sources (Traitement du signal) |0 (CaQQLa)000266787 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Mechanical. |2 bisacsh | |
| 650 | 7 | |a Blind source separation |2 fast |0 (OCoLC)fst01739925 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128177969 |z Texto completo |