@BOOK\{IMM2004-03397,
    author       = "A. Barros and J. Principe and J. Larsen and T. Adali and S. C. Douglas",
    title        = "Proceedings of {IEEE} Workshop on Machine Learning for Signal Processing {XIV}",
    year         = "2004",
    month        = "sep",
    keywords     = "machine learning signal processing neural networks",
    volume       = "",
    number       = "",
    series       = "",
    publisher    = "{IEEE} Press",
    address      = "Piscataway, New Jersey",
    edition      = "",
    url          = "http://isp.imm.dtu.dk/mlsp2004",
    abstract     = "This proceeding contains refereed papers presented at the fourteenth {IEEE} Workshop on Machine for Signal Processing (MLSP’2004), held at S\~{a}o Luís, Maranh\~{a}o, Brazil,  September 29-October {1,} 2004.  This is a continuation of the {IEEE} workshops on Neural Networks for Signal Processing (NNSP) organized by the {NNSP} technical committee of the Signal Processing society. The name of the technical committee, hence of the workshop, has been 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 last year, 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, 171 full papers (10 pages) were submitted, out of which 79 (resulting in an acceptance rate of approximately 50\%) were selected for oral or poster presentation, after reviews by three referees for each. We would like to thank the {MLSP}’2004 Technical Committee for taking the time to provide quality reviews. Special thanks also go to Drs. Guilherme Barreto, Osvaldo Saavedra, and Hani Yehia of the Organizing Committee for their commitment, handling of the workshop budget, registration, and the {CDROM} copy of the proceedings.

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, speech processing, as well as implementation and other applications of machine learning. We would like to express our appreciation and gratitude to {UFMA,} {EMAP,} {ELETROBR}Á{S,} {ELETRONORTE,} {ALUMAR} and {BASA,} who contributed to the workshop by providing technical and financial support in various forms. Our warmest, special thanks go to our plenary speakers: Prof. Petar M. Djuric of Stony Brook University (USA), Prof. Sun-Yuan Kung of Princeton University (USA), and Prof. Erkki Oja of Helsinki University of Technology (Finland). 

Continuing the tradition of paperless and easy communication, many of the details of the {MLSP}’2004 Workshop were handled electronically through the workshop webpage (http://isp.imm.dtu.dk/mlsp2004), which, among other features, included web-based submissions, review, and registration.",
    isbn_issn    = "0-7803-8609-4"
}