---
title: Interior-Point Linear SVMs
url: https://www.ml-quant.com/papers/repec/spr-joptap-v-202-y-2024-i-1-d-10-1007-s10957-022-02103-1/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spr:joptap:v:202:y:2024:i:1:d:10.1007_s10957-022-02103-1
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10957-022-02103-1%3Bh%3Drepec%3Aspr%3Ajoptap%3Av%3A202%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10957-022-02103-1
featured: 2024-07-31
citations: unknown
topic: Other
---


# Interior-Point Linear SVMs

The paper uses multiple variable splitting to solve binary classification and novelty detection problems in high-dimensional data, demonstrating competitive results against other methods and specific algorithms.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10957-022-02103-1%3Bh%3Drepec%3Aspr%3Ajoptap%3Av%3A202%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10957-022-02103-1
- Identifier: RePEc:spr:joptap:v:202:y:2024:i:1:d:10.1007_s10957-022-02103-1
- Released: 2024-07-31
- First featured: Quant Letter No. 59 (2024-07-31): https://www.ml-quant.com/issues/2024-07-31/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Other

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