2017 Workshop on Signal Processing and Machine Learning
The workshop will bring together engineers, students, practitioners, and researchers from the fields of machine learning (ML) and signal processing (SP). The aim of the workshop is to contribute to the cross-fertilization between the research on ML methods and their application to SP to initiate collaboration between these areas. ML usually plays an important role in the transition from data storage to decision systems based on large databases of signals such as the obtained from sensor networks, internet services, or communication systems. These systems imply developing both computational solutions and novel models. Signals from real-world systems are usually complex such as speech, music, bio-medical, multimedia, among others. Thus, SP techniques are very useful for these type of systems to automate processing and analysis techniques to retrieve information from data storage. Topics will range from foundations for real-world systems, and processing, such as speech, language analysis, biomedicine, convergence and complexity analysis, machine learning, social networks, sparse representations, visual analytics, robust statistical methods.
Ricardo Rodriguez Jorge (Autonomous University of Ciudad Juarez, Mexico)
Jolanta Mizera-Pietraszko (Opole University, Poland)
Ezendu Ariwa (University of Bedfordshire, United Kindom)
Jiri Bila, (Czech Technical University in Prague, Czech Republic)
Ke Liao (University of Kansas, USA)
Mohamed Elgendi (University of British Columbia, Canada)
Nghien N. B., (Hanoi University of Industry, Vietnam)
Pit Pichappan, (Al Imam University, Saudi Arabia)
Yao-Liang Chung (National Taipei University, Taiwan)
For more information please click "Further Official Information" below.