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Meta-Learning: Strategies, Implementations, and Evaluations for Algorithm Selection: Volume 91 Dissertation in Artificial Intelligence
by Christian Rudolf Kopf
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
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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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Product Details
- ISBN-13:
- 9781586035624
- Publisher:
- IOS Press, Incorporated
- Publication date:
- 11/01/2005
- Pages:
- 250
- Product dimensions:
- 5.80(w) x 8.20(h) x 0.60(d)
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