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Multiple Classifier Systems - 6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, Proceedings (Paperback, 2005 ed.)
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Multiple Classifier Systems - 6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, Proceedings (Paperback, 2005 ed.)
Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics, 3541
Expected to ship within 10 - 15 working days
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The belief that a committee of people make better decisions than
any individual is widely held and appreciated. We also understand
that, for this to be true, the members of the committee have to be
simultaneously competent and comp- mentary. This intuitive notion
holds true for committees of data sources (such as sensors) and
models (such as classi?ers). The substantial current research in
the areas of data fusion and model fusion focuses on ensuring that
the di?- ent sources provide useful information but nevertheless
complement one another to yield better results than any source
would on its own. During the 1990s, a variety of schemes in
classi?er fusion, which is the focus of this workshop, were
developed under many names in di?erent scienti?c communities such
as machine learning, pattern recognition, neural networks, and
statistics. The previous ?ve workshops on Multiple Classi?er
Systems (MCS) were themselves exercises in information fusion, with
the goal of bringing the di?erent scienti?c commu- ties together,
providing each other with di?erent perspectives on this fascinating
topic, and aiding cross-fertilization of ideas. These ?ve workshops
achieved this goal, demonstrating signi? cant advances in the
theory, algorithms, and appli- tions of multiple classi?er systems.
Followingits?vepredecessorspublishedbySpringer,thisvolumecontainsthe
proceedings of the 6th International Workshop on Multiple Classi?er
Systems
(MCS2005)heldattheEmbassySuitesinSeaside,California,USA,June13-15,
2005. Forty-two papers were selected by the Scienti?c Committee,
and they were organized into the following sessions: Boosting,
Combination Methods, Design of Ensembles, Performance Analysis, and
Applications.
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