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A Relational Framework for Classifier Engineering

Summary: Formal relational framework modeling feature engineering as database queries to enable DB-centric analysis of classifier expressivity and learnability. Defines separability, VC-dimension of query-defined feature classes, and identifiability; analyzes complexity for conjunctive-query features under common syntactic restrictions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1714
Venue
PODS
Year
2017
Pagerank
5.1019568e-05
Overall Rank
6,347 | 55.85%
DOI
10.1145/3034786.3034797

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
10,269 Database Views as Explanations for Relational Deep Learning 2026 VLDB 4.1945683e-05
10,324 Towards Efficient Random-Order Enumeration for Join Queries 2026 VLDB 4.1945683e-05
11,157 Extremal Fitting Problems for Conjunctive Queries 2023 PODS 4.1945683e-05
11,639 Regularizing Conjunctive Features for Classification 2019 PODS 4.1945683e-05
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Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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