English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Kernel method for percentile feature extraction

Schölkopf, B., Platt, J., & Smola, A.(2000). Kernel method for percentile feature extraction (MSR-TR-2000-22). Redmond, WA, USA: Microsoft Research, Microsoft Corporation.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E5E4-A Version Permalink: http://hdl.handle.net/21.11116/0000-0005-B6FB-3
Genre: Report

Files

show Files

Creators

show
hide
 Creators:
Schölkopf, B1, Author              
Platt, JC, Author
Smola, AJ, Author              
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: A method is proposed which computes a direction in a dataset such that a specied fraction of a particular class of all examples is separated from the overall mean by a maximal margin The pro jector onto that direction can be used for classspecic feature extraction The algorithm is carried out in a feature space associated with a support vector kernel function hence it can be used to construct a large class of nonlinear fea ture extractors In the particular case where there exists only one class the method can be thought of as a robust form of principal component analysis where instead of variance we maximize percentile thresholds Fi nally we generalize it to also include the possibility of specifying negative examples

Details

show
hide
Language(s):
 Dates: 2000-02
 Publication Status: Published in print
 Pages: -
 Publishing info: Redmond, WA, USA : Microsoft Research, Microsoft Corporation
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: MSR-TR-2000-22
BibTex Citekey: 1836
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show