A classification model in machine learning based on centroids
Rocchio Classification
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.[1]
An extended version of the nearest centroid classifier has found applications in the medical domain, specifically classification of tumors.[2]
^Manning, Christopher; Raghavan, Prabhakar; Schütze, Hinrich (2008). "Vector space classification". Introduction to Information Retrieval. Cambridge University Press.
^Tibshirani, Robert; Hastie, Trevor; Narasimhan, Balasubramanian; Chu, Gilbert (2002). "Diagnosis of multiple cancer types by shrunken centroids of gene expression". Proceedings of the National Academy of Sciences. 99 (10): 6567–6572. doi:10.1073/pnas.082099299. PMC 124443. PMID 12011421.
and 14 Related for: Nearest centroid classifier information
In machine learning, a nearestcentroidclassifier or nearest prototype classifier is a classification model that assigns to observations the label of...
model, though they both contain similar origins. Nearestcentroidclassifier, aka Rocchio classifier Christopher D. Manning, Prabhakar Raghavan, Hinrich...
approximately similar size, as they will always assign an object to the nearestcentroid. This often leads to incorrectly cut borders of clusters (which is...
Generated by centroid-edge midpoint construction by polygon-centroid-vertex detection, rounding the angle of each co-edge to the nearest 15 degrees. Since...
may increase the variance of the classifier and is very likely to discard useful or important samples. Cluster centroids is a method that replaces cluster...
dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive datasets into a more compact...
features to each sample, where each feature j has value one iff the jth centroid learned by k-means is the closest to the sample under consideration. It...
model such as BERT. The question vectors are clustered. Questions nearest to the centroids of each cluster are selected. An LLM does zero-shot CoT on each...
cluster with the nearest mean. Another version is the k-medoids algorithm, which, when selecting a cluster center or cluster centroid, will pick one of...
Hedrick, Brandon P.; Ezcurra, Martin D. (18 January 2016). "Figure 4: Centroid size regression analyses for the main sample". PeerJ. 4: e1589. doi:10...
evolution and occurs close to the middle of morphological variation (the centroid of morphospace) among related taxa (i.e. a species is morphologically conservative...
between the data and the corresponding cluster centroid. Thus the purpose of K-means clustering is to classify data based on similar expression. K-means clustering...
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