Probabilistic Classification and Clustering in Relational Data

By B. Taskar, Eran Segal, Daphne Koller. International Joint Conference on Artificial Intelligence, 2001

299 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/311f963e8bee972f13b9dc6c59f139ee3c733582

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Citations in the corpus

Why collective inference improves relational classification (2004, background): “While only a few studies [1,9,16] have actively varied the percentage of known labels, the results above closely parallel those of Macskassy and Provost [9], who show that the relative advantage of an iterative inference procedure over a noniterative procedure reduces as the percentage of labeled da”
Link Prediction in Relational Data (2003, methodology): “Link graph structure has also bee n used to improve document classification [7, 6, 15].”
A Simple Relational Classifier (2003, background): “, Probabilistic Relational Models (PRMs) [16, 10, 27], Relational Probability Trees (RPTs) [22] and Relational Bayesian Classifiers (RBCs) [23]—search the relational space for useful attributes and relational structure of neighbors (possibly more than one link away).”
Discriminative Probabilistic Models for Relational Data (2002, methodology): “Taskar et al. (2001) suggest the use of probabilistic rela tional models (PRMs) for the collective classification task.”
Leveraging relational autocorrelation with latent group models (2005, background): “use of latent variables in PRMs has been explored in limited settings where the groups are known and represented as objects in the data [22].”
Social Network Data Analytics (2011, methodology): “Among many approaches proposed within the SRL framework, two that have been directly applied to the node classi cation problem are Probabilistic Relational Models (PRMs) [10, 33] and Relational Markov Network Models (RMNs) [32].”
Collective Classification in Network Data (2008, background): “…and David Jensen (Neville and Jensen 2000; Jensen, Neville, and Gallagher 2004; Neville and Jensen 2007) and the work of Ben Taskar and colleagues (Taskar, Segal , and Koller 2001; Getoor et al. 2001; Taskar, Abbeel, and Koller 2002; Taskar, Guestrin, and Koller 2003), the general problem of…”
Link-Based Classification (2003, background): “, 2000) and for relational learning (Neville Jensen, 2000; Taskar et al., 2001; Taskar et al., 2002).”
Leveraging social media networks for classification (2011, methodology): “For instance, probabilistic relational model (PRM) as introduced in [ 44 ,43].”
Collective Classi!cation in Network Data (2008, background): “…and David Jensen (Neville and Jensen 2000; Jensen, Neville, and Gallagher 2004; Neville and Jensen 2007) and the work of Ben Taskar and colleagues (Taskar, Segal , and Koller 2001; Getoor et al. 2001; Taskar, Abbeel, and Koller 2002; Taskar, Guestrin, and Koller 2003), the general problem of…”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, unknown): “In a prior study, Taskar et al. (2001) show that a relational Bayesian network (RBN), there called a Probabilistic Relational Model (PRM), using author and citation links in conjunction with the text of the paper, was able to achieve a higher accuracy than a non-relational naive Bayesian classiﬁer…”