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Kan Deng
PhD Student

No longer a member of RI.

For more information, see my personal homepage.

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My research focuses primarily on learning to recognize time series.

machine learning and pattern recognition

• Auton Lab - We build practical large-scale deployments of very highly autonomous self-improving systems.

• Auton Project - Research into Autonomous Learning Software Systems

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• OMEGA: ON-LINE MEMORY-BASED GENERAL PURPOSE SYSTEM CLASSIFIER
  K. Deng
  doctoral dissertation, tech. report CMU-RI-TR-98-33, Robotics Institute, Carnegie Mellon University, November, 1998. [Abstract]
  Download: pdf [3319 KB] copyrighted

• On the Greediness of Feature Selection Algorithms
  K. Deng and A. Moore
  International Conference of Machine Learning (ICML '98), June, 1998. [Abstract]
  Download: pdf [67 KB], ps.gz [185 KB] copyrighted

• On Greediness of Feature Selection Algorithms
  K. Deng and A. Moore
  tech. report CMU-RI-TR-98-03, Robotics Institute, Carnegie Mellon University, February, 1998. [Abstract]
  Download: pdf [67 KB], ps.gz [185 KB] copyrighted

• On-line Memory-based Detection of General Purpose Systems
  K. Deng, A. Moore, and M. Nechyba
  Neural Information Systems Processing 1998 (NIPS '98), 1998.
  Download: pdf [0 KB], ps.gz [84 KB] copyrighted

• Efficient Locally Weighted Polynomial Regression Predictions
  A. Moore, J. Schneider, and K. Deng
  International Conference on Machine Learning, 1997.
  Download: pdf [245 KB], ps.gz [146 KB] copyrighted

• Learning to Recognize Time Series: Combining ARMA models with Memory-based Learning
  K. Deng, A. Moore, and M. Nechyba
  IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, Vol. 1, 1997, pp. 246 - 250. [Abstract]
  Download: pdf [461 KB], ps.gz [223 KB] copyrighted

• Multiresolution Instance-Based Learning
  A. Moore, J. Schneider, and K. Deng
  Proceedings of International Joint Conference on Artificial Intelligence, 1995.
  Download: pdf [77 KB], ps.gz [52 KB] copyrighted