Buch, Englisch, 388 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 676 g
Applications, Challenges, and the Road Ahead
Buch, Englisch, 388 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 676 g
ISBN: 978-0-367-61892-6
Verlag: CRC Press
Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML).
ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML.
The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML.
FEATURES
- Focuses on addressing the missing connection between signal processing and ML
- Provides a one-stop guide reference for readers
- Oriented toward material and flow with regards to general introduction and technical aspects
- Comprehensively elaborates on the material with examples and diagrams
This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.
Zielgruppe
Academic, Postgraduate, Professional, and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Mathematik | Informatik EDV | Informatik Technische Informatik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
- Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
Weitere Infos & Material
1. Introduction to Signal Processing and Machine Learning
Kavitha Somaraj
2. Learning Theory (Supervised/Unsupervised) for Signal Processing
Ruby Jain, Bhuvan Jain, and Manimala Puri
3. Supervised and Unsupervised Learning Theory for Signal Processing
Sowmya K. B.
4. Applications of Signal Processing
Anuj Kumar Singh and Ankit Garg
5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing
V. Ajantha Devi and Mohd Naved
6. Brain–Computer Interfacing
Paras Nath Singh
7. Adaptive Filters and Neural Net
Sowmya K. B., Chandana G., and Anjana Mahaveer Daigond
8. Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network
Saikat Majumder
9. Intelligent Video Surveillance Systems Using Deep Learning Methods
Anjanadevi Bondalapati and Manjaiah D. H.
10. Stationary Signal, Autocorrelation, and Linear and Discriminant Analysis
Bandana Mahapatra and Kumar Sanjay Bhorekar
11. Intelligent System for Fault Detection in Rotating Electromechanical Machines.
Pascal Dore, Saad Chakkor, and Ahmed El Oualkadi
12. Wavelet Transformation and Machine Learning Techniques for Digital Signal Analysis in IoT Systems
Rajalakshmi Krishnamurthi and Dhanalekshmi Gopinathan