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Bibliography

References to publications, sorted by year and author.

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2000	Rätsch, i, Schölkopf, B, Smola, AJ, Müller, K, Onoda, T, and Mika, S (2000). $\nu $-Arc: Ensemble Learning in the Presence of Outliers In: Proc. NIPS 12 , ed. by S. A. Solla and T. K. Leen and K.-R. Müller, pp. 561-567, MIT Press.

Shin, H, Jang, M, Cho, S, and Lee, B (2000).
Generalization Abilities of Ensemble Learning Algorithms
Proc. of the Korean Brain Society Conference.

Shin, H, Jang, M, Cho, S, Lee, B, and Lim, Y (2000).
Generalization Abilities of Ensemble Learning Algorithms: OLA, Bagging, Boosting
Proc. of the 27th Korea Information Science Society Conference:226-228.

Shin, H, Lee, H, and Cho, S (2000).
Observational Learning with Modular Networks
Lecture Notes in Computer Science, 1983:126-132.

Shin, H, Lee, H, and Cho, S (2000).
Ensemble Learning Algorithm of Specialized Networks
Proc. of the Korea Information Science Society Conference:308-310.

Shin, H, Lee, H, and Cho, S (2000).
Ensemble of Specialized Networks based on Input Space Partition
Proc. of the Korean Operations Research and Management Science Society Conference:33-36.

Tai, S, Ong, C, and Abdullah, NA (2000).
On Designing an Automated Malaysian Stemmer for the Malay Language
In: Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages.

Zien, A, Rätsch, G, Mika, S, Schölkopf, B, Lengauer, T, and Müller, K (2000).
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA
Bioinformatics, 16(9):799-807.

1999	Maisuria, L, Ong, CS, and Lai, W (1999). A Comparison of Artificial Neural Networks and Cluster Analysis for Typing Biometrics Authentication In: International Joint Conference on Neural Networks.

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, and Müller, K (1999).
Fisher Discriminant Analysis with Kernels
In: Proc. NNSP'99, ed. by Y. - H. Hu, J. Larsen, E. Wilson and S. Douglas, pp. 41-48, IEEE.

Mika, S, Schölkopf, B, Smola, AJ, Müller, K, Scholz, M, and Rätsch, G (1999).
Kernel PCA and De-Noising in Feature Spaces
In: Proc. NIPS 11, ed. by M. S. Kearns, S. A. Solla and D. A. Cohn, pp. 536-542, MIT Press.

Müller, K, Philips, P, and Ziehe, A (1999).
JADETD: Combining higher-order statistics and temporal information for blind source separation (with noise)
In: ICA '99, ed. by J.F. Cardoso and Ch. Jutten and Ph. Loubaton, pp. 87-92, Aussois, France.

Philips, P (1999).
Source Separation Using Temporal Information and Higher-Order Statistics
Master thesis, Technical University of Karlsruhe, Germany.

Rätsch, G, Onoda, T, and Müller, K (1999).
Regularizing AdaBoost
In: Proc. NIPS 11, ed. by M. S. Kearns, S. A. Solla and D. A. Cohn, pp. 564-570, MIT Press.

Schölkopf, B, Mika, S, Burges, CJ, Knirsch, P, Müller, K, Rätsch, G, and Smola, AJ (1999).
Input Space vs. Feature Space in Kernel-Based Methods
IEEE Transanctions on Neural Networks, 10(5):1000-1017.

Schubert, W, Koutzevlov, A, Horn, E, Rätsch, G, and Tschapek, A (1999).
Aspekte der Flexibilisierung von Systemen für den Hardwaretest
University of Potsdam, Potsdam.

Smola, A, Schölkopf, B, and Rätsch, G (1999).
Linear Programs for Automatic Accuracy Control in Regression
In: Proc. ICANN'99, Berlin, Springer-Verlag.

Zien, A, Rätsch, G, Mika, S, Schölkopf, CL, Smola, AJ, Lengauer, T, and Mueller, K (1999).
Engineering Support Vector Machine Kernel That Recognize Translation Initiation Sites in DNA
In: Proceedings GCB'99.

1998	Onoda, T, Rätsch, G, and Müller, K (1998). An asymptotic analysis of AdaBoost in the binary classification case In: Proc. ICANN'98, ed. by L. Niklasson, M.Bodén and T. Ziemke, pp. 195-200.

Rätsch, G, Onoda, T, and Müller, K (1998).
Soft Margins for AdaBoost
Royal Holloway College, NeuroCOLT, Technical Report(NC-TR-1998-021), University of London.

Rätsch, G, Onoda, T, and Müller, K (1998).
An improvement of AdaBoost to avoid overfitting
In: Proc. ICONIP, pp. 506-509, Kitakyushu, Japan.

Rätsch, G (1998).
Ensemble Learning Methods for Classification
Diplom thesis, University of Potsdam, Neues Palais 10, Potsdam.

Printable file

Schölkopf, B, Mika, S, Smola, AJ, Rätsch, G, and Müller, K (1998).
Kernel PCA Pattern Reconstruction via Approximate Pre-Images
In: Proceedings of the 8th International Conference on Artificial Neural Networks, ed. by L. Niklasson, M.Bodén and T. Ziemke, pp. 147-152, Berlin, Springer Verlag.

Shin, H (1998).
Qualitative Modeling for Data Miner's Requirement
Master thesis, Seoul National University.

Shin, H and Jhee, W (1998).
Qualitative Modeling for Data Miner's Requirements
Proc. of the Korean Society of Management Information Systems Conference.

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