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Regularizing Conjunctive Features for Classification

Summary: Study of generating conjunctive-feature queries that linearly separate labeled entities, introducing regularizers on feature dimension, join count, and generalized hypertree width (ghw), plus approximate variants allowing bounded misclassification. Main results: separability is tractable for bounded ghw, explicit feature-generation is intractable due to potential query size, but classification can be done efficiently without materializing features under bounded ghw. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1742
Venue
PODS
Year
2019
Pagerank
4.1945683e-05
Overall Rank
11,639 | 19.03%
DOI
10.1145/3294052.3319680

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Rank Citing Paper Year Venue Pagerank
11,157 Extremal Fitting Problems for Conjunctive Queries 2023 PODS 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
690 An Analytical Study of Large SPARQL Query Logs 2018 VLDB 0.00018099792
761 Materialization Optimizations for Feature Selection Workloads 2014 SIGMOD 0.00017053783
1,328 Hypertree Decompositions: Questions and Answers 2016 PODS 0.00012565612
6,347 A Relational Framework for Classifier Engineering 2017 PODS 5.1019568e-05
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