Independent Component Analysis and Signal Separation [electronic resource] : 7th International Conference, ICA 2007, London, UK, September 9-12, 2007. Proceedings / edited by Mike E. Davies, Christopher J. James, Samer A. Abdallah, Mark D Plumbley.

By: Davies, Mike E [editor.]Contributor(s): James, Christopher J [editor.] | Abdallah, Samer A [editor.] | Plumbley, Mark D [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 4666Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007Description: XIX, 847 p. Also available online. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540744948Subject(s): Computer science | Coding theory | Computer software | Data mining | Mathematical statistics | Computer Science | Algorithm Analysis and Problem Complexity | Computation by Abstract Devices | Coding and Information Theory | Statistics and Computing/Statistics Programs | Data Mining and Knowledge Discovery | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 LOC classification: QA76.9.A43Online resources: Click here to access online
Contents:
Theory -- Algorithms -- Sparse Methods -- Speech and Audio Applications -- Biomedical Applications -- Miscellaneous -- Keynote Talk.
In: Springer eBooksSummary: This volume contains the papers presented at the 7th International Conference on Independent Component Analysis (ICA) and Source Separation held in L- don, 9–12 September 2007, at Queen Mary, University of London. Independent Component Analysis and Signal Separation is one of the most exciting current areas of research in statistical signal processing and unsup- vised machine learning. The area has received attention from several research communities including machine learning, neural networks, statistical signal p- cessing and Bayesian modeling. Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series. This year’s event was organized by the EPSRC-funded UK ICA Research Network (www.icarn.org). There was also a minor change to the conference title this year with the exclusion of the word‘blind’. The motivation for this was the increasing number of interesting submissions using non-blind or semi-blind techniques that did not really warrant this label. Evidence of the continued interest in the ?eld was demonstrated by the healthy number of submissions received, and of the 149 papers submitted just over two thirds were accepted.
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Theory -- Algorithms -- Sparse Methods -- Speech and Audio Applications -- Biomedical Applications -- Miscellaneous -- Keynote Talk.

This volume contains the papers presented at the 7th International Conference on Independent Component Analysis (ICA) and Source Separation held in L- don, 9–12 September 2007, at Queen Mary, University of London. Independent Component Analysis and Signal Separation is one of the most exciting current areas of research in statistical signal processing and unsup- vised machine learning. The area has received attention from several research communities including machine learning, neural networks, statistical signal p- cessing and Bayesian modeling. Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series. This year’s event was organized by the EPSRC-funded UK ICA Research Network (www.icarn.org). There was also a minor change to the conference title this year with the exclusion of the word‘blind’. The motivation for this was the increasing number of interesting submissions using non-blind or semi-blind techniques that did not really warrant this label. Evidence of the continued interest in the ?eld was demonstrated by the healthy number of submissions received, and of the 149 papers submitted just over two thirds were accepted.

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