Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Elastic Net Regularized Logistic Regression using Cubic Majorization

In English

Författare

Summary, in English

In this work, a coordinate solver for elastic net regularized logistic regression is proposed. In particular, a method based on majorization maximization using a cubic function is derived. This to reliably and accurately optimize the objective function at each step without resorting to line search. Experiments show that the proposed solver is comparable to, or improves, state-of-the-art solvers. The proposed method is simpler, in the sense that there is no need for any line search, and can directly be used for small to large scale learning problems with elastic net regularization.

Avdelning/ar

Publiceringsår

2014

Språk

Engelska

Sidor

3446-3451

Publikation/Tidskrift/Serie

2014 22nd International Conference on Pattern Recognition (ICPR)

Dokumenttyp

Konferensbidrag

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Computational Mathematics

Conference name

22nd International Conference on Pattern Recognition (ICPR 2014)

Conference date

2014-08-24 - 2014-08-28

Conference place

Stockholm, Sweden

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 1051-4651