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- Q6489154 subject Q7015116.
- Q6489154 subject Q8382009.
- Q6489154 abstract "Large margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization.The goal of supervised learning (more specifically classification) is to learn a decision rule that can categorize data instances into pre-defined classes. The k-nearest neighbor rule assumes a training data set of labeled instances (i.e. the classes are known). It classifies a new data instance with the class obtained from the majority vote of the k closest (labeled) training instances. Closeness is measured with a pre-defined metric. Large margin nearest neighbors is an algorithm that learns this global (pseudo-)metric in a supervised fashion to improve the classification accuracy of the k-nearest neighbor rule.".
- Q6489154 thumbnail Lmnn.png?width=300.
- Q6489154 wikiPageExternalLink mnist.
- Q6489154 wikiPageExternalLink 2012_05_03_MachineLearning_lecture_09.pdf.
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- Q6489154 wikiPageWikiLink Q7015116.
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- Q6489154 wikiPageWikiLink Q8382009.
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- Q6489154 comment "Large margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization.The goal of supervised learning (more specifically classification) is to learn a decision rule that can categorize data instances into pre-defined classes. The k-nearest neighbor rule assumes a training data set of labeled instances (i.e.".
- Q6489154 label "Large margin nearest neighbor".
- Q6489154 depiction Lmnn.png.
