Conference item
In-database learning with sparse tensors
- Abstract:
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In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of stat...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Accepted manuscript, pdf, 690.5KB)
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- Publisher copy:
- 10.1145/3196959.3196960
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Authors
Bibliographic Details
- Publisher:
- Association for Computing Machinery Publisher's website
- Journal:
- ACM Principles of Database Systems Journal website
- Pages:
- 325-340
- Host title:
- SIGMOD/PODS '18 Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
- Publication date:
- 2018-05-27
- Acceptance date:
- 2017-09-01
- Event location:
- Houston
- DOI:
- Source identifiers:
-
725630
- ISBN:
- 9781450347068
Item Description
- Keywords:
- Pubs id:
-
pubs:725630
- UUID:
-
uuid:2d852e0d-889d-46fe-890e-b1ac5687c798
- Local pid:
- pubs:725630
- Deposit date:
- 2017-09-05
Terms of use
- Copyright holder:
- Abo Khamis et al
- Copyright date:
- 2018
- Notes:
-
Copyright © 2018 the authors. Publication rights licensed to the
Association for Computing Machinery. This is the accepted manuscript version of the article. The final version is available online from ACM at: https://doi.org/10.1145/3196959.3196960
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