Why a complex valued solution for a real domain problem

Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Volumn , Issue , 2007, Pages 384-389

  Mandic, Danilo P ^a   Javidi, Soroush ^a   Souretis, George ^a   Goh, Vanessa S L ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; EQUIVALENCE CLASSES; IMAGE PROCESSING; IMAGING SYSTEMS; IMAGING TECHNIQUES; OPTICAL DATA PROCESSING; ROBOT LEARNING; SIGNAL PROCESSING; VECTORS;

CASE STUDIES; COMPLEX NUMBERS; DOMAIN PROBLEMS; IEEE SIGNAL PROCESSING SOCIETY; MACHINE-LEARNING; MODERN APPLICATIONS; MULTI-CHANNEL MEASUREMENTS; POINT PROCESSES; POTENTIAL BENEFITS; REAL-VALUED DATA; VECTOR FIELDS;

LEARNING SYSTEMS;

EID: 48149085333     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2007.4414337     Document Type: Conference Paper

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