Exact Indexing for Support Vector Machines
Summary: Proposes iKernel, exact index for SVM top-k queries with varying kernel parameters. Kernel-space ranking instability and ordering stability enable pruning; kernel clustering boosts effectiveness; first exact top-k without full data scan (1–5% eval). (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Hwanjo Yu
- 2. Ilhwan Ko
- 3. Youngdae Kim
- 4. Seungwon Hwang
- 5. Wook-Shin Han
Incoming Citations (Sorted by Pagerank)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015436578 |
| 17 | Optimizing Multi-Feature Queries for Image Databases | 2000 | VLDB | 0.00095799341 |
| 91 | M-tree: An Efficient Access Method for Similarity Search in Metric Spaces | 1997 | VLDB | 0.00051785122 |
| 3,911 | Progressive and Selective Merge: Computing Top-K with Ad-hoc Ranking Functions | 2007 | SIGMOD | 6.6343292e-05 |
| 7,469 | Boolean + Ranking: Querying a Database by K-Constrained Optimization | 2006 | SIGMOD | 4.7165172e-05 |
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