Towards Semi-supervised Manifold Learning: UKR with Structural Hints

Jan Steffen, Stefan Klanke, Sethu Vijayakumar, Helge Ritter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These new extensions are targeted at representing a dextrous manipulation task. We thus evaluate the effectiveness of the proposed mechanisms on appropriate toy data that mimic the characteristics of the aimed manipulation task and thereby provide means for a systematic evaluation.
Original languageEnglish
Title of host publicationAdvances in Self-Organizing Maps
Subtitle of host publication7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings
PublisherSpringer-Verlag GmbH
Pages298-306
Number of pages8
ISBN (Print)978-3-642-02396-5
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
Volume5629
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Informatics
  • Computer Science

Fingerprint

Dive into the research topics of 'Towards Semi-supervised Manifold Learning: UKR with Structural Hints'. Together they form a unique fingerprint.

Cite this