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Nearest centroid classifier

In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation. When applied to text classification using word vectors containing tf*idf weights to represent documents, the nearest centroid classifier is known as the Rocchio classifier because of its similarity to the Rocchio algorithm for relevance feedback.

Algorithm
Training Given labeled training samples \textstyle\{(\vec{x}_1, y_1), \dots, (\vec{x}_n, y_n)\} with class labels y_i \in \mathbf{Y}, compute the per-class centroids \textstyle\vec{\mu}_\ell = \frac{1}\underset{i \in C_\ell}{\sum} \vec{x}_i where C_\ell is the set of indices of samples belonging to class \ell \in \mathbf{Y}. Prediction The class assigned to an observation \vec{x} is \hat{y} = {\arg\min}_{\ell \in \mathbf{Y}} \|\vec{\mu}_\ell - \vec{x}\|. == See also ==
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