Multiple Classifier Systems

Nikunj C. Oza

ISBN 10: 3540263063 / ISBN 13: 9783540263067

Published by Springer Berlin Heidelberg Jun 2005, 2005

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 444 pp. Englisch.

Seller Inventory # 9783540263067

Bibliographic Details

• Title: Multiple Classifier Systems
• Publisher: Springer Berlin Heidelberg Jun 2005
• Publication Date: 2005
• Language: English
• ISBN 10: 3540263063
• ISBN 13: 9783540263067
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 24D millimeters 235 millimeters width by 155 millimeters height by 24 millimeters depth
• Weight: 668 grams

About this title

Synopsis

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.