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The Rocchio algorithm is based on a method of relevance feedback found in information retrieval systems which stemmed from the SMART Information Retrieval System developed between 1960 and 1964. Like many other retrieval systems, the Rocchio algorithm was developed using the vector space model .
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 is closest to the observation.
The slow "standard algorithm" for k-means clustering, and its associated expectation–maximization algorithm, is a special case of a Gaussian mixture model, specifically, the limiting case when fixing all covariances to be diagonal, equal and have infinitesimal small variance.
The relevance feedback information needs to be interpolated with the original query to improve retrieval performance, such as the well-known Rocchio algorithm. A performance metric which became popular around 2005 to measure the usefulness of a ranking algorithm based on the explicit relevance feedback is normalized discounted cumulative gain.
Many important concepts in information retrieval were developed as part of research on the SMART system, including the vector space model, relevance feedback, and Rocchio classification. Gerard Salton led the group that developed SMART. Other contributors included Mike Lesk.
This technique is based on several algorithms, including the Rocchio algorithm and the evolutionary algorithm. The Rocchio algorithm, [10] locating a query point near relevant examples and far away from irrelevant examples, is simple and works well in a small system where the databases are arranged in certain ranks. The evolutionary synthesis ...
Shortstop Brayan Rocchio singled home automatic runner Ramón Laureano with one out in the 10th inning, giving the Cleveland Guardians a 5-4 victory over the Detroit Tigers on Wednesday. Cleveland ...
Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation). [57]