Probabilistic Classification and Clustering in Relational Data

by: Benjamin Taskar, Eran Segal, Daphne Koller

edited by: Bernhard Nebel

(2001), pp. 870-878.

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摘要

Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often...

@inproceedings{citeulike:780466, abstract = {Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often...}, address = {Seattle, US}, author = {Taskar, Benjamin and Segal, Eran and Koller, Daphne }, booktitle = {Proceeding of IJCAI-01, 17th International Joint Conference on Artificial Intelligence}, citeulike-article-id = {780466}, editor = {Nebel, Bernhard }, keywords = {clustering, relational}, pages = {870--878}, posted-at = {2006-07-30 17:08:07}, priority = {2}, title = {Probabilistic Classification and Clustering in Relational Data}, url = {http://citeseer.ist.psu.edu/taskar01probabilistic.html}, year = {2001} }

RIS record

TY - CONF ID - citeulike:780466 L3 - citeulike-article-id:780466 N2 - Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often... AD - Seattle, US EP - 878 TI - Probabilistic Classification and Clustering in Relational Data T2 - Proceeding of IJCAI-01, 17th International Joint Conference on Artificial Intelligence SP - 870 KW - clustering KW - relational AU - Taskar, Benjamin AU - Segal, Eran AU - Koller, Daphne A2 - Nebel, Bernhard PY - 2001/// UR - http://citeseer.ist.psu.edu/taskar01probabilistic.html ER -

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