English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Painless Embeddings of Distributions: the Function Space View

Smola, A., Gretton, A., & Fukumizu, K. (2008). Painless Embeddings of Distributions: the Function Space View. Talk presented at 25th International Conference on Machine Learning (ICML 2008). Helsinki, Finland. 2008-07-05 - 2008-07-09.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C8A9-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-A7C2-5
Genre: Talk

Files

show Files

Locators

show
hide
Locator:
http://icml2008.cs.helsinki.fi/papers/icml2008proceedings.pdf (Abstract)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Smola, A, Author           
Gretton, A1, 2, Author           
Fukumizu, K, 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: This tutorial will give an introduction to the recent understanding and methodology of the kernel method: dealing with higher order statistics by embedding painlessly random variables/probability distributions.
In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear algorithms from linear ones. More recently, however, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics by embedding distributions in a suitable reproducing kernel Hilbert space (RKHS). Notably, unlike the straightforward expansion of higher order moments or conventional characteristic function approach, the use of kernels or RKHS provides a painless, tractable way of embedding distributions.
This line of reasoning leads naturally to the questions: what does it mean to embed a distribution in an RKHS? when is this embedding injective (and thus, when do different distributions have unique mappings)? what implications are there for learning algorithms that make use of these embeddings? This tutorial aims at answering these questions.
There are a great variety of applications in machine learning and computer science, which require distribution estimation and/or comparison.

Details

show
hide
Language(s):
 Dates: 2008-07
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5271
 Degree: -

Event

show
hide
Title: 25th International Conference on Machine Learning (ICML 2008)
Place of Event: Helsinki, Finland
Start-/End Date: 2008-07-05 - 2008-07-09

Legal Case

show

Project information

show

Source 1

show
hide
Title: ICML 2008: 25th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
McCallum , A, Editor
Roweis, S, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : Oregon State University
Pages: - Volume / Issue: - Sequence Number: T1 Start / End Page: XXXVII Identifier: ISBN: 978-1-60558-205-4