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Hardcover. Etat : Very Good. No Jacket. Hardcover 2005 library bound edition. Ex-library book with stamps and labels attached. Binding firm. Pages unmarked and clean. Laminated covers and text in very good to near fine condition. Series: Lecture Notes in Computer Science, ;3541.[xii, 430 p. : ill. ; 24 cm].

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Paperback. Etat : new. Paperback. 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. Constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005. This book contains papers that are organized in topical sections on boosting, combination methods, performance analysis, and applications. They exemplify the advances in the theory and applications of multiple classifier systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Taschenbuch. Etat : Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 444 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Paperback. Etat : new. Paperback. 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. Constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005. This book contains papers that are organized in topical sections on boosting, combination methods, performance analysis, and applications. They exemplify the advances in the theory and applications of multiple classifier systems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Etat : Neu. 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.

Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Future Directions.- Semi-supervised Multiple Classifier Systems: Background and Research Directions.- Boosting.- Boosting GMM and Its Two Applications.- Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection.- Observations on Boosting Feat.