Advances in Machine Learning and Signal Processing Proceedings of MALSIP 2015
by
 
Soh, Ping Jack. editor.

Title
Advances in Machine Learning and Signal Processing Proceedings of MALSIP 2015

Author
Soh, Ping Jack. editor.

ISBN
9783319322131

Physical Description
IX, 312 p. 139 illus., 88 illus. in color. online resource.

Series
Lecture Notes in Electrical Engineering, 387

Abstract
This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learning and signal processing for engineering problems. .

Subject Term
Engineering.
 
Computer vision.
 
Signal, Image and Speech Processing. http://scigraph.springernature.com/things/product-market-codes/T24051
 
Computational Intelligence. http://scigraph.springernature.com/things/product-market-codes/T11014
 
Image Processing and Computer Vision. http://scigraph.springernature.com/things/product-market-codes/I22021

Added Author
Soh, Ping Jack.
 
Woo, Wai Lok.
 
Sulaiman, Hamzah Asyrani.
 
Othman, Mohd Azlishah.
 
Saat, Mohd Shakir.

Added Corporate Author
SpringerLink (Online service)

Electronic Access
https://doi.org/10.1007/978-3-319-32213-1


LibraryMaterial TypeItem BarcodeShelf NumberStatus
IYTE LibraryE-Book2087844-1001TK5102.9Online Springer