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Titlebook: Meta-Learning in Decision Tree Induction; Krzysztof Gr?bczewski Book 2014 Springer International Publishing Switzerland 2014 Computational

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發(fā)表于 2025-3-21 19:56:44 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Meta-Learning in Decision Tree Induction
編輯Krzysztof Gr?bczewski
視頻videohttp://file.papertrans.cn/632/631196/631196.mp4
概述Presents a general meta-learning approach which is applicable to a variety of machine learning algorithms.Focuses on different variants of decision tree induction.Details the long and complex road fro
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Meta-Learning in Decision Tree Induction;  Krzysztof Gr?bczewski Book 2014 Springer International Publishing Switzerland 2014 Computational
描述.The book focuses on different variants of decision tree induction but also describes ?the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehen
出版日期Book 2014
關(guān)鍵詞Computational Intelligence; Machine Learning Decision Tree Induction; Meta-Learning
版次1
doihttps://doi.org/10.1007/978-3-319-00960-5
isbn_softcover978-3-319-37723-0
isbn_ebook978-3-319-00960-5Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Future Perspectives of Meta-Learning, the ultimate goal should always be an improvement in learning at base-level. Even the most attractive form of meta-knowledge is not a value for itself, but only if it can help improve learning processes, so that learning at object-level gets faster or more accurate.
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發(fā)表于 2025-3-22 07:29:40 | 只看該作者
Introduction,Decision trees (DTs) belong to the most commonly used computational intelligence (CI) models. Even when other algorithms provide more accurate models (better approximating the target), DTs are often regarded as very attractive.
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發(fā)表于 2025-3-22 10:25:18 | 只看該作者
Techniques of Decision Tree Induction,Finding optimal DT for given data is not easy (with exceptions of some trivial cases). The hierarchical structure of DT models could suggest that the optimization process is also nicely reduced with subsequent splits, but it is not so.
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