2017 Workshop on Signal Processing and Machine Learning (SiPML)

Страна: Испания

Город: Barcelona

Тезисы до: 15.07.2017

Даты: 08.11.17 — 10.11.17

Е-мейл Оргкомитета: ricardo.jorge at uacj.mx

Организаторы: Open University of Catalonia


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.
•Learning theory
•Cognitive information processing
•Neural networks
•Classification and pattern recognition
•Nonlinear signal processing
•Graphical models and kernel methods
•Genomic signals and  sequences
•Multichannel adaptive signal processing
•Kernel methods and graphical models
•Sparsity-aware learning
•Subspace/maniforld learning
•Bayesian and distributed learning
•Smart Grid, games, social networks
•Computational Intelligence
•Data-driven adaptive systems
•Data-driven models
•Multimodal data fusion
•Multiset data analysis
•Perceptual signal processing
•Applications (biomedical signals, biometrix, bioinformatics)


Веб-сайт конференции: http://www.sipml.com.mx/default.html

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