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The book explores the Small Sample Size Problem, particularly in high-dimensional data contexts where the number of training samples is insufficient for effective learning. It highlights the challenges faced in fields like face recognition, where this issue is prevalent. The author discusses various strategies and methodologies for addressing and mitigating the effects of having too few samples, aiming to enhance data analysis and improve outcomes in high-dimensional scenarios.
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Global k-NN Classifier for Small Sample Size Problems, Nan Zhang
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- Pubblicato
- 2018
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