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The aim is to improve outlier detection methods for multidimensional nonlinear functions while reducing computational costs.

Outlier Detection Using Support Vector Machines

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The aim is to improve outlier detection methods for multidimensional nonlinear functions while reducing computational costs.
Proposed an approach using support vector regression for outlier detection.
Defined outlier thresholds appropriately to enhance detection efficacy.
Applied the methodology to real data examples for validation.
Achieved reduced computational time compared to traditional methods.
Successfully defined outlier thresholds leading to better detection results.
Demonstrated the practical applicability of the method on real datasets.

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Randomized trial demonstrates effective outlier detection using support vector regression in real data, suggesting improved computational efficiency.

Seo et al. (2011) studied this question.

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Randomized trial demonstrates effective outlier detection using support vector regression in real data, suggesting improved computational efficiency.