tailieunhanh - Báo cáo hóa học: " Editorial Emerging Machine Learning Techniques in Signal Processing"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Editorial Emerging Machine Learning Techniques in Signal Processing | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 830381 2 pages doi 2008 830381 Editorial Emerging Machine Learning Techniques in Signal Processing Theodoros Evgeniou 1 Aníbal R. Figueiras-Vidal 2 and Sergios Theodoridis3 1 Technology Management and Decision Sciences INSEAD Boulevard de Constance 77300 Fontainebleau France 2 Department of Signal Theory and Communications Carlos III University of Madrid 28911 Leganes Madrid Spain 3 Deptartment of Informatics and Telecommunications Division of Communications and Signal Processing Panepistimiopolis Ilissia Athens 15784 Greece Correspondence should be addressed to Sergios Theodoridis stheodor@ Received 28 July 2008 Accepted 28 July 2008 Copyright 2008 Theodoros Evgeniou et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In the era of knowledge-based society and machine automation there is a strong interest in machine learning ML techniques in a wide range of applications. The attention paid to ML methods within the DSP community is not new. Speech recognition is an example of an area where DSP and machine learning have been combined to develop efficient and robust speech recognizers. Channel equalization is another area at the intersection of ML and DSP techniques. After all deciding upon the transmitted information symbol is nothing but a class assignment task. In cognitive radio DSP techniques and ML methods can work together for developing algorithms for the efficient utilization of the radio spectrum. Image video audio coding recognition and retrieval are some additional typical examples where DSP and ML tie together. Another problem at the heart of the DSP community interests is the regression task which can be cast as an ML problem. Biomedical applications constitute another area