English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H., & Seeger, M.(2011). Multiple Kernel Learning: A Unifying Probabilistic Viewpoint. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BC70-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-93A7-B
Genre: Report

Files

show Files
hide Files
:
MPIK-TR-2011-Nickisch.pdf (Any fulltext), 603KB
Name:
MPIK-TR-2011-Nickisch.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show
hide
Locator:
https://arxiv.org/abs/1103.0897 (Any fulltext)
Description:
-

Creators

show
hide
 Creators:
Nickisch, H1, Author              
Seeger, M, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

Content

show
hide
Free keywords: -
 Abstract: We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm.

Details

show
hide
Language(s):
 Dates: 2011-03
 Publication Status: Published in print
 Pages: 12
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: NickischS2011
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

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