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Searching for authors named "Jennifer Neville" – sorted by Relevance.

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  • Autocorrelation and linkage cause bias in evaluation of relational learners  
  • by David Jensen, Jennifer Neville — 2002 — In Proceedings of the Twelfth International Conference on Inductive Logic Programming
  • …Two common characteristics of relational data sets — concentrated linkage and relational auto-correlation — can cause traditional methods of evaluation to greatly overestimate the accuracy of induced models on test sets. We identify these characteristics, define quantitative measures of their severi…
  • Cited by 9 (3 self) – Add To MetaCart
  • Schemas and Models  
  • by David Jensen, Jennifer Neville — 2002 — In Proceedings of the SIGKDD-2002 Workshop on Multi-relational Learning
  • …We propose the Schema-Model Framework, which characterizes algorithms that learn probabilistic models from relational data as having two parts: a schema that identifies sets of related data items and groups them into relevant categories; and a model that allows probabilistic inference about those da…
  • Cited by 9 (1 self) – Add To MetaCart
  • Data Mining in Social Networks  
  • by David Jensen, Jennifer Neville — 2002 — In National Academy of Sciences Symposium on Dynamic Social Network Modeling and Analysis
  • …Abstract. Several techniques for learning statistical models have been developed recently by researchers in machine learning and data mining. All of these techniques must address a similar set of representational and algorithmic choices and must face a set of statistical challenges unique to learnin…
  • Cited by 16 (1 self) – Add To MetaCart
  • Correlation and sampling in relational data mining  
  • by David Jensen, Jennifer Neville — 2001 — Proceedings of the 33rd Symposium on the Interface of Computing Science and Statistics
  • …Data mining in relational data poses unique opportunities and challenges. In particular, relational autocorrelation provides an opportunity to increase the predictive power of statistical models, but it can also mislead investigators using traditional sampling approaches to evaluate data mining algo…
  • Cited by 1 (1 self) – Add To MetaCart
  • Leveraging relational autocorrelation with latent group models  
  • by Jennifer Neville, David Jensen — 2005 — In Proceedings of the 5th IEEE International Conference on Data Mining
  • …The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. Recent resea…
  • Cited by 17 (4 self) – Add To MetaCart
  • Dependency Networks for Relational Data  
  • by Jennifer Neville, David Jensen — 2004 — In Proceedings of the 4th IEEE International Conference on Data Mining
  • …Instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the references of a paper. Recent work on graphical models for relational data has demonstrated…
  • Cited by 40 (5 self) – Add To MetaCart
  • Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning  
  • by David Jensen, J. Neville, Jennifer Neville — 2002 — In Proceedings of the 19th International Conference on Machine Learning
  • …Two common characteristics of relational data sets --- concentrated linkage and relational autocorrelation --- can cause learning algorithms to be strongly biased toward certain features, irrespecti e of their predicti e power. We identify these characteristics, define quantitati e measures of…
  • Cited by 53 (16 self) – Add To MetaCart
  • Simple Estimators for Relational Bayesian Classifiers  
  • by J. Neville, Jennifer Neville, D. Jensen, B. Gallagher, R. Fairgrieve — 2003 — In Proceedings of the 3rd IEEE International Conference on Data Mining
  • …This paper evaluates several modifications of the Simple Bayesian Classifier to enable estimation and inference over relational data. The resulting Relational Bayesian Classifiers are evaluated on three real-world datasets and compared to a baseline SBC using no relational information…
  • Cited by 24 (7 self) – Add To MetaCart
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