English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Infinite Kernel Learning

Gehler, P., & Nowozin, S.(2008). Infinite Kernel Learning (178). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C6CD-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8704-1
Genre: Report

Files

show Files
hide Files
:
MPIK-TR-178.pdf (Publisher version), 294KB
Name:
MPIK-TR-178.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Gehler, PV1, 2, Author              
Nowozin, S1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: In this paper we consider the problem of automatically learning the kernel from general kernel classes. Specifically we build upon the Multiple Kernel Learning (MKL) framework and in particular on the work of (Argyriou, Hauser, Micchelli, Pontil, 2006). We will formulate a Semi-Infinite Program (SIP) to solve the problem and devise a new algorithm to solve it (Infinite Kernel Learning, IKL). The IKL algorithm is applicable to both the finite and infinite case and we find it to be faster and more stable than SimpleMKL (Rakotomamonjy, Bach, Canu, Grandvalet, 2007) for cases of many kernels. In the second part we present the first large scale comparison of SVMs to MKL on a variety of benchmark datasets, also comparing IKL. The results show two things: a) for many datasets there is no benefit in linearly combining kernels with MKL/IKL instead of the SVM classifier, thus the flexibility of using more than one kernel seems to be of no use, b) on some datasets IKL yields impressive increases in accuracy over SVM/MKL due to the possibility of using a largely increased kernel set. In those cases, IKL remains practical, whereas both cross-validation or standard MKL is infeasible.

Details

show
hide
Language(s):
 Dates: 2008-10
 Publication Status: Published in print
 Pages: 12
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 178
BibTex Citekey: 5617
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 178 Sequence Number: - Start / End Page: - Identifier: -