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Abstract
The probabilistic classification vector machine is a very effective and generic probabilistic and sparse classifier. A recently published incremental version improved the runtime complexity to quadratic costs. We derive the Nyström approximation for asymmetric matrices to obtain linear runtime and memory complexity for the incremental probabilistic classification vector machine while keeping similar prediction performance.
Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 8 |
ISBN (Print) | 9781479919604 |
DOIs | |
Publication status | Published - 28 Sept 2015 |
Event | International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 12 Jul 2015 → 17 Jul 2015 |
Conference
Conference | International Joint Conference on Neural Networks, IJCNN 2015 |
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Country/Territory | Ireland |
City | Killarney |
Period | 12/07/15 → 17/07/15 |
Keywords
- Support vector machines
- Xenon
ASJC Scopus subject areas
- Software
- Artificial Intelligence
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Dive into the research topics of 'Incremental probabilistic classification vector machine with linear costs'. Together they form a unique fingerprint.Projects
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Personalised Medicine through Learning in the Model Space
Engineering & Physical Science Research Council
1/10/13 → 31/03/17
Project: Research Councils