@BOOK\{IMM2005-04040,
    author       = "V. Calhoun and T. Adali and J. Larsen and D. Miller and S. Douglas",
    title        = "Proceedings of {IEEE} Machine Learning for Signal Processing Workshop {XV}",
    year         = "2005",
    month        = "sep",
    keywords     = "machine learning signal processing",
    volume       = "",
    number       = "",
    series       = "",
    publisher    = "IEEE",
    address      = "Piscataway, New Jersey",
    edition      = "",
    url          = "http://mlsp2005.conwiz.dk",
    abstract     = "These proceedings contains refereed papers presented at the Fifteenth {IEEE} Workshop on
Machine Learning for Signal Processing (MLSP’2005), held in Mystic, Connecticut, {USA,} 
September 28-30, 2005. This is a continuation of the {IEEE} Workshops on Neural Networks for
Signal Processing (NNSP) organized by the {NNSP} Technical Committee of the {IEEE} Signal
Processing Society. The name of the Technical Committee, hence of the Workshop, was changed
to Machine Learning for Signal Processing in September 2003 to better reflect the areas
represented by the Technical Committee. The conference is organized by the Machine Learning
for Signal Processing Technical Committee with sponsorship of the {IEEE} Signal Processing
Society.
Following the practice started two years ago, the bound volume of the proceedings is going to be
published by {IEEE} following the Workshop, and we are pleased to offer to conference attendees
the proceeding in a {CDROM} electronic format, which maintains the same standard as the printed
version and facilitates the reading and searching of the papers
The field of machine learning has matured considerably in both methodology and real-world
application domains and has become particularly important for solution of problems in signal
processing. As reflected in this collection, machine learning for signal processing combines many
ideas from adaptive signal/image processing, learning theory and models, and statistics in order to
solve complex real-world signal processing applications. High quality across such topical
diversity can only be maintained through a rigorous and selective review process. This year, 119
full papers (6 pages) were submitted, out of which 65 (resulting in an acceptance rate of 55\%)
were selected for oral or poster presentation, after reviews by three referees for each. We would
like to thank the {MLSP}’2005 Technical Committee for taking the time to provide quality reviews.
This year, the workshop featured research work in the areas of nonlinear signal processing,
system identification, blind source separation, learning theory and models, neural networks,
applications in image and video processing and speech processing, as well as implementation and
other applications of machine learning. Two special sessions on bioinformatics and biomedical
imaging and data fusion were included in the program as well as a tutorial on engineering aspects
of fixed point theory. This was also the first year for a data competition which was chaired by
Deniz Erdogmus. Our warmest, special thanks go to our plenary speakers: Prof. Andrew Barron
of Yale University (USA), Prof. Barry Horwitz of the National Institutes of Health (USA) and
Prof. Simon Haykin of McMaster University (Canada).
Continuing the tradition of paperless and easy communication, many of the details of the
 {MLSP}’2005 Workshop were handled electronically through the workshop webpage
(http://mlsp2005.conwiz.dk), which, among other features, included web-based submissions,
review, and registration.",
    isbn_issn    = "{ISBN} {0-}7803-9518-{2,} {ISSN} 1551-2"
}