New Algorithms for Monotone Classification
Summary: Active model: exact optimal needs Ω(n) label probes even for d=1; randomized (1+ε)-approximation uses Õ(w/ε^2) probes (w = dominance width), matching lower bounds up to polylog factors. Passive model: optimal monotone classifier found in poly(n,d) time. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 319 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00027781866 |
| 509 | On Active Learning of Record Matching Packages | 2010 | SIGMOD | 0.00021409518 |
| 643 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018754451 |
| 712 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00017732426 |
| 2,767 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD | 8.1513883e-05 |
| 3,528 | Distributed Data Deduplication | 2016 | VLDB | 7.0066139e-05 |
| 3,712 | MOMA - A Mapping-based Object Matching System | 2007 | CIDR | 6.823134e-05 |
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