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Book Meta-learning: Strategies, Implementations, and Evaluations for Algorithm Selection: 91 (Dissertations in Artificial Intelligence: Infix)

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Meta-learning: Strategies, Implementations, and Evaluations for Algorithm Selection: 91 (Dissertations in Artificial Intelligence: Infix)

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    Available in PDF - DJVU Format | Meta-learning: Strategies, Implementations, and Evaluations for Algorithm Selection: 91 (Dissertations in Artificial Intelligence: Infix).pdf | Language: ENGLISH
    C.R. Kopf(Author)

    Book details


Data analysis via supervised learning tasks is among the most common data mining techniques. The objective of meta-learning is to generate a user-supporting system for selection of the most appropriate supervised learning algorithms for such tasks. The meta-learning framework is usually based upon a classification on the meta-level often disregarding a large amount of information gained during the induction process. The performance of supervised learning algorithms is also clearly dependent on the quality of the data. And, considering only a small subset of meta-attributes may significantly reduce both the time and effort applied for the corresponding measurement process. In this book, the extent to which the issues above impact the performance of a meta-learning system is evaluated and solutions for remedying the difficulties observed are presented. In particular, the accuracies of the base learners are predicted, thus avoiding the rigid decision on a single-best learner. Subsequently, the severity of data quality issues is investigated.
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Book details

  • PDF | 250 pages
  • C.R. Kopf(Author)
  • IOS Press,US (14 Mar. 2007)
  • English
  • 9
  • Computing & Internet

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