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The fusion of different information sources has long been a compelling issue across various disciplines, including political science, statistics, computer science, and communications. Pioneers like Laplace and von Neumann laid the groundwork for this field. In recent years, the focus has shifted towards pattern recognition, particularly in the 1990s, when classifier fusion schemes, especially at the decision level, gained prominence under various names in machine learning, neural networks, and statistics. The differing terminologies reflect the unique perspectives and cultural backgrounds of these communities, as well as the lack of common forums for sharing results. In 1999, the first workshop on multiple classifier systems was organized to foster an international platform for disseminating achievements and establishing a unified terminology, thereby adding concrete value to the diverse viewpoints. Over five workshops, significant progress has been made, with researchers from various fields actively participating and world experts presenting comprehensive surveys of the state of the art. This collaborative effort has facilitated cross-fertilization of ideas and knowledge among the different scientific communities involved.
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Multiple classifier systems, Fabio Roli
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- 2004
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