English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Learning Output Kernels with Block Coordinate Descent

Dinuzzo, F., Ong, C. S., Gehler, P., & Pillonetto, G. (2011). Learning Output Kernels with Block Coordinate Descent. In L. Getoor, & T. Scheffer (Eds.), Proceedings of the 28th Internationl Conference on Machine Learning (pp. 49-56). Madison, WI: Omnipress. Retrieved from http://www.icml-2011.org/papers/54_icmlpaper.pdf.

Item is

Files

show Files
hide Files
:
dinuzzo11output-kernel.pdf (Any fulltext), 804KB
 
File Permalink:
-
Name:
dinuzzo11output-kernel.pdf
Description:
-
OA-Status:
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Dinuzzo, Francesco1, Author
Ong, Cheng Soon1, Author
Gehler, Peter2, Author           
Pillonetto, Gianluigi1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

Content

show
hide
Free keywords: -
 Abstract: We propose a method to learn simultaneously a vector-valued function and a kernel between its components. The obtained kernel can be used both to improve learning performance and to reveal structures in the output space which may be important in their own right. Our method is based on the solution of a suitable regularization problem over a reproducing kernel Hilbert space of vector-valued functions. Although the regularized risk functional is non-convex, we show that it is invex, implying that all local minimizers are global minimizers. We derive a block-wise coordinate descent method that efficiently exploits the structure of the objective functional. Then, we empirically demonstrate that the proposed method can improve classification accuracy. Finally, we provide a visual interpretation of the learned kernel matrix for some well known datasets.

Details

show
hide
Language(s): eng - English
 Dates: 20112011
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 618786
URI: http://www.icml-2011.org/papers/54_icmlpaper.pdf
Other: Local-ID: C12576EE0048963A-E26EA7E320ADF143C12578C500513D2B-dinuzzo2011icml
 Degree: -

Event

show
hide
Title: 28th Internationl Conference on Machine Learning
Place of Event: Bellevue, Wash.
Start-/End Date: 2011-06-28 - 2011-07-02

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 28th Internationl Conference on Machine Learning
  Abbreviation : ICML 2011
Source Genre: Proceedings
 Creator(s):
Getoor, Lise1, Editor
Scheffer, Tobias2, Editor           
Affiliations:
1 External Organizations, ou_persistent22            
2 Machine Learning, MPI for Informatics, Max Planck Society, ou_1116552            
Publ. Info: Madison, WI : Omnipress
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 49 - 56 Identifier: ISBN: 978-1-4503-0619-5