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Email:
sangkyun.lee
cs.uni-dortmund.de
Phone: 0231/755-6490 Fax: 0231/755-5105 Room-No.: R 1.47 |
| Lee/Wright/2013a |
Lee, Sangkyun and Wright, StephenJ..
Stochastic Subgradient Estimation Training for Support Vector Machines.
In
Latorre Carmona, Pedro and S\'anchez, J. Salvador and Fred, Ana L.N. (editors),
Mathematical Methodologies in Pattern Recognition and Machine Learning,
Vol. 30,
No. null,
pages 67--82,
Springer,
2013.
|
| Lee/2012a |
Lee, Sangkyun.
Improving Confidence of Dual Averaging Stochastic Online Learning via Aggregation.
In
German Conference on Artificial Intelligence (KI 2012),
No. null,
pages 229--232,
2012.
|
| Lee/etal/2012a |
Lee, Sangkyun and Stolpe, Marco and Morik, Katharina.
Separable Approximate Optimization of Support Vector Machines for Distributed Sensing.
In
Peter Flach and Tijl De Bie and Nello Cristianini (editors),
Machine Learning and Knowledge Discovery in Databases,
Vol. 7524,
pages 387--402,
Berlin, Heidelberg,
Springer,
2012.
|
| Lee/Wright/2012a |
Lee, Sangkyun and Wright, Stephen J..
ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines.
In
International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012),
pages 223-228,
2012.
|
| Lee/Wright/2012b |
Lee, Sangkyun and Wright, Stephen J..
Manifold Identification in Dual Averaging Methods for Regularized Stochastic Online Learning.
In
Journal of Machine Learning Research,
Vol. 13,
pages 1705--1744,
2012.
|
| Piatkowski/etal/2012a |
Piatkowski, Nico and Lee, Sangkyun and Morik, Katharina.
Spatio-Temporal Models For Sustainability.
In
Marwah, Manish and Ramakrishnan, Naren and Berges, Mario and Kolter, Zico (editors),
Proceedings of the SustKDD Workshop within ACM KDD 2012,
ACM,
2012.
|
| Umaashankar/Lee/2012a | Umaashankar, Venkatesh and Lee, Sangkyun. Optimization plugin for RapidMiner. No. 4, TU Dortmund University, 2012. |
| Lee/2011a | Lee, Sangkyun. Optimization Methods for Regularized Convex Formulations in Machine Learning. University of Wisconsin--Madison, 2011. |
| Lee/Bockermann/2011a |
Lee, Sangkyun and Bockermann, Christian.
Scalable stochastic gradient descent with improved confidence.
In
Big Learning -- Algorithms, Systems, and Tools for Learning at Scale,
2011.
|
| Lee/etal/2011a | Lee, Sangkyun and Schowe, Benjamin and Sivakumar, Viswanath and Morik, Katharina. Feature Selection for High-Dimensional Data with RapidMiner. No. 1, TU Dortmund University, 2011. |
| Lee/Wright/2011a |
Lee, Sangkyun and Wright, Stephen J..
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning.
In
the 28th International Conference on Machine Learning,
2011.
|
| Lee/Wright/2011b |
Lee, Sangkyun and Wright, Stephen J..
Manifold Identification in Dual Averaging Methods for Regularized Stochastic Online Learning.
University of Wisconsin-Madison,
2011.
|
| Lee/Wright/2009a |
Lee, Sangkyun and Wright, Stephen J..
Decomposition Algorithms for Training Large-scale Semiparametric Support Vector Machines.
In
Buntine, Wray and Grobelnik, Marko and Mladenic, Dunja and Shawe-Taylor, John (editors),
Machine Learning and Knowledge Discovery in Databases,
Vol. 5782,
pages 1-14,
Springer,
2009.
|
| Lee/Wright/2009b | Lee, Sangkyun and Wright, Stephen J.. Signal Processing Algorithms on Graphical Processing Units. In INFORMS Annual Meeting, 2009. |
| Lee/Wright/2009c |
Lee, Sangkyun and Wright, Stephen J..
Decomposition and Stochastic Subgradient Algorithms for Support Vector Machines.
In
20th International Symposium on Mathematical Programming,
2009.
|
| Lee/Wright/2008a |
Lee, Sangkyun and Wright, Stephen J..
Implementing Algorithms for Signal and Image Reconstruction on Graphical Processing Units.
University of Wisconsin-Madison,
2008.
|
| Lee/etal/2006a |
Lee, Sangkyun and Lee, S.-J. and Zhang, B.-T..
Combining Information-based Supervised and Unsupervised Feature Selection.
In
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh (editors),
Feature Extraction: Foundations and Applications,
Springer,
2006.
|
| Lee/2005a | Lee, Sangkyun. Integrating Different Species Pairwise Information Using Multiple Relational Embedding Techniques. Seoul National University, 2005. |