| 书目名称 | Meta-Learning in Computational Intelligence | | 编辑 | Norbert Jankowski,Włodzisław Duch,Krzysztof Gra̧bc | | 视频video | | | 概述 | Recent research in Meta-learning in computational intelligence.Presents new Developments and Trends in Computational Intelligence and Learning.Written by leading experts in the field | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | .Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. .Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. .This is where algorithms that learn how to learnl come to rescue. .Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of kn | | 出版日期 | Book 2011 | | 关键词 | Computational Intelligence; Meta-learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-20980-2 | | isbn_softcover | 978-3-642-26858-8 | | isbn_ebook | 978-3-642-20980-2Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer Berlin Heidelberg 2011 |
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发表于 2025-3-21 17:25:18
