| 书目名称 | Multiple Classifier Systems | | 副标题 | Second International | | 编辑 | Josef Kittler,Fabio Roli | | 视频video | | | 概述 | Includes supplementary material: | | 丛书名称 | Lecture Notes in Computer Science | | 图书封面 |  | | 描述 | Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, | | 出版日期 | Conference proceedings 2001 | | 关键词 | Algorithmic Learning; Bagging; Boosting; Classification; Classifier SYstems; Document Analysis; Image ANal | | 版次 | 1 | | doi | https://doi.org/10.1007/3-540-48219-9 | | isbn_softcover | 978-3-540-42284-6 | | isbn_ebook | 978-3-540-48219-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer-Verlag Berlin Heidelberg 2001 |
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发表于 2025-3-21 16:38:58
