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R. Min, D. A. Stanley, Z. Yuan, A. Bonner, and Z. Zhang. A Deep Non-Linear Feature Mapping for Large-Margin kNN Classification. IEEE International Conference on Data Mining (ICDM 2009). Oral presentation. Acceptance rate: 70/786. |
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R. Min, J. Li, R. Kuang, A. Bonner, and Z. Zhang. Adaptive Kernel Methods for Sequence Motif Discovery. In preparation. |
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Y. Liu, R. Min, J. Li, and Z. Zhang. Comparative analysis of the genome-wide transcript data reveals a large number of small open reading frames under purifying selection in human genome. To be submitted. |
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R. Min, A. Bonner, J. Li, and Z. Zhang. Learned Random-Walk Kernels and Empirical-Map Kernels for Protein Sequence Classification. Journal of Computational Biology. March 2009, 16(3): 457-474. |
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J. Li*, R. Min*, A. Bonner, and Z. Zhang. (*Co-first authors) A Probabilistic Framework to Improve microRNA Target Prediction by Incorporating Proteomics Data. Journal of Bioinformatics and Computational Biology. To appear. 2009. |
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R. Min, R. Kuang, A. Bonner, and Z. Zhang. Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery. 2009 SIAM International Conference on Data Mining (SDM09). Full paper and oral presentation. Acceptance rate: 55/351. |
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R. Min, A. Bonner, and Z. Zhang. Modifying kernels using label information improves SVM classification performance. IEEE Proceeding of the 2007 International Conference on Machine Learning and Applications. December 13-15, 2007, pp 13-18. |
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R. Min A non-linear dimensionality reduction method for improving nearest neighbour classification. Master Thesis. Department of Computer Science, University of Toronto. 2005. |
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R. Min, D. A. Stanley, Z. Yuan, A. Bonner, and Z. Zhang. Large-Margin kNN Classification Using a Deep Encoder Network. arxiv [pdf]. June 2009. |
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R. Min. Adaptive KNN Classification Based on Laplacian Eigenmaps and Kernel Mixtures. Technical Report. Department of Computer Science. University of Toronto. Feb 2008. |
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R. Min, A. Bonner and Z. Zhang Modifying Kernels Using Label Information Improves Protein Classification Performance. Department of Computer Science, University of Toronto. 2006. |
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R. Min. Motion Interpretation by ICA for the course Machine Learning in Computer Graphics. Department of Computer Science. University of Toronto. 2006. |
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R. Min. A Suvery on Context-Based Computer Vision Systems. for the course Object Modeling and Recognition. Department of Computer Science, University of Toronto. 2006. |
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G. Hinton and R. Min Regularized Autoencoder Networks. Department of Computer Science, University of Toronto. 2005. Results [ps][pdf]. |