Towards Semi-supervised Manifold Learning: UKR with Structural Hints

Jan Steffen; Stefan Klanke; Sethu Vijayakumar; Helge Ritter

doi:10.1007/978-3-642-02397-2_34

Towards Semi-supervised Manifold Learning: UKR with Structural Hints

Jan Steffen, Stefan Klanke, Sethu Vijayakumar, Helge Ritter

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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 language	English
Title of host publication	Advances in Self-Organizing Maps
Subtitle of host publication	7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings
Publisher	Springer-Verlag GmbH
Pages	298-306
Number of pages	8
ISBN (Print)	978-3-642-02396-5
DOIs	https://doi.org/10.1007/978-3-642-02397-2_34
Publication status	Published - 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg
Volume	5629
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords / Materials (for Non-textual outputs)

Informatics
Computer Science

Access to Document

10.1007/978-3-642-02397-2_34

http://www.springerlink.com/content/k451684040143110/

Cite this

Steffen, J., Klanke, S., Vijayakumar, S., & Ritter, H. (2009). Towards Semi-supervised Manifold Learning: UKR with Structural Hints. In Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings (pp. 298-306). (Lecture Notes in Computer Science; Vol. 5629). Springer-Verlag GmbH. https://doi.org/10.1007/978-3-642-02397-2_34

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title = "Towards Semi-supervised Manifold Learning: UKR with Structural Hints",

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.",

keywords = "Informatics, Computer Science",

author = "Jan Steffen and Stefan Klanke and Sethu Vijayakumar and Helge Ritter",

year = "2009",

doi = "10.1007/978-3-642-02397-2_34",

language = "English",

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Steffen, J, Klanke, S, Vijayakumar, S & Ritter, H 2009, Towards Semi-supervised Manifold Learning: UKR with Structural Hints. in Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5629, Springer-Verlag GmbH, pp. 298-306. https://doi.org/10.1007/978-3-642-02397-2_34

Towards Semi-supervised Manifold Learning: UKR with Structural Hints. / Steffen, Jan; Klanke, Stefan; Vijayakumar, Sethu et al.
Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings. Springer-Verlag GmbH, 2009. p. 298-306 (Lecture Notes in Computer Science; Vol. 5629).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Towards Semi-supervised Manifold Learning: UKR with Structural Hints

AU - Steffen, Jan

AU - Klanke, Stefan

AU - Vijayakumar, Sethu

AU - Ritter, Helge

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Informatics

KW - Computer Science

UR - http://www.scopus.com/inward/record.url?scp=69049086735&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-02397-2_34

DO - 10.1007/978-3-642-02397-2_34

M3 - Conference contribution

SN - 978-3-642-02396-5

T3 - Lecture Notes in Computer Science

SP - 298

EP - 306

BT - Advances in Self-Organizing Maps

PB - Springer-Verlag GmbH

ER -

Towards Semi-supervised Manifold Learning: UKR with Structural Hints

Abstract / Description of output

Publication series

Keywords / Materials (for Non-textual outputs)

Access to Document

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