Abbrevation
SiPML
City
Taichung
Country
Taiwan
Deadline Paper
Start Date
End Date
Abstract

The 4th International Workshop on Signal Processing and Machine Learning (SiPML 2018) is organized by Dr&#046; Ricardo Rodriguez, Dr&#046; Jolanta Mizera&#8211;Pietraszko from Autonomous University of Ciudad Juarez, and Opole University, respectively&#046; SiPML will be held in Tunghai University, Taichung, Taiwan on Octorber 27&#8211;29, 2018, in conjunction with the main conference 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2018)&#046;<br>The aim of the workshop is to contribute to the cross&#8211;fertilization between the research on Machine Learning (ML) methods and their application to Signal Processing (SP) to initiate collaboration between these areas&#046; 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&#046; These systems imply developing both computational solutions and novel models&#046; Signals from real&#8211;world systems are usually complex such as speech, music, bio&#8211;medical, and multimedia, among others&#046;<br>1&#046; Topics<br>Topics of interest include (but not limited to):<br>&#8211; Learning theory<br>&#8211; Subspace/maniforld learning<br>&#8211; Cognitive information processing<br>&#8211; Bayesian and distributed learning<br>&#8211; Neural networks<br>&#8211; Smart Grid, games, social networks<br>&#8211; Classification and pattern recognition<br>&#8211; Computational Intelligence<br>&#8211; Nonlinear signal processing<br>&#8211; Data&#8211;driven adaptive systems<br>&#8211; Graphical models and kernel methods<br>&#8211; Data&#8211;driven models<br>&#8211; Genomic signals and sequences<br>&#8211; Multimodal data fusion<br>&#8211; Multichannel adaptive signal processing<br>&#8211; Multiset data analysis<br>&#8211; Kernel methods and graphical models<br>&#8211; Perceptual signal processing<br>&#8211; Sparsity&#8211;aware learning<br><div>&#8211; Applications (biomedical signals, biometrix, bioinformatics)</div><div><br></div>