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大间隔最近邻居（Large margin nearest neighbor (LMNN)）分类算法是统计学的一种。该算法是在近邻分类其中学习一种欧式距离度量函数。该度量函数优化的目标是：对于一个输入 的个近邻都属于同一类别，而不同类别的样本与保持一定大的距离。近邻规则是模式识别领域广泛使用的一种简单有效的方法。它的效果的好坏只依赖于确定最近邻的距离度量。基于欧式距离度量学习函数的大间隔最近邻居分类算法能够很好的改善近邻算法分类效果。
, Large margin nearest neighbor (LMNN) class … Large margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm for metric learning. 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.n accuracy of the k-nearest neighbor rule.
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Large margin nearest neighbor (LMNN) class … Large margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization.mming, a sub-class of convex optimization.
, 大间隔最近邻居（Large margin nearest neighbor (LMNN)）分类算法是统计学的一种。该算法是在近邻分类其中学习一种欧式距离度量函数。该度量函数优化的目标是：对于一个输入 的个近邻都属于同一类别，而不同类别的样本与保持一定大的距离。近邻规则是模式识别领域广泛使用的一种简单有效的方法。它的效果的好坏只依赖于确定最近邻的距离度量。基于欧式距离度量学习函数的大间隔最近邻居分类算法能够很好的改善近邻算法分类效果。
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Large margin nearest neighbor
, 大间隔最近邻居
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