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Results: 295
Number of items: 295
  • ten Hagen, S. H. G., & Kröse, B. J. A. (2001). Autonomous map building by mobile robots. In B. Kröse, M. de Rijke, G. Schreiber, & M. van Someren (Eds.), Proceedings of the 13th Dutch-Belgian Artificial Intelligence Conference, BNAIC' 2001 (pp. 117-124)
  • Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2001). Efficient greedy learning of Gaussian mixtures. In B. Kröse, M. de Rijke, G. Schreiber, & M. van Someren (Eds.), Proceedings of the 13th Belgian-Dutch Conference on Artificial Intelligence, BNAIC' 01 (pp. 251-258).
  • Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2001). Greedy Gaussian mixture learning for texture segmentation. In A. Leonardis, & H. Bischof (Eds.), ICANN' 01, Workshop on Kernel and Subspace Methods for Computer Vision (pp. 37-47).
  • Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2001). A soft k-segments algorithm for principal curves. In Proceedings of the International Conference on Artificial Neural Networks (pp. 450-456).
  • Open Access
    ten Hagen, S. H. G. (2001). Continuous State Space Q-Learning for control of Nonlinear Systems. [Thesis, fully internal, Computer Science Institute, University of A' dam].
  • ten Hagen, S. H. G., & Kröse, B. J. A. (2000). Neural Q-Learning. (IAS-UVA report; No. 00-09). Informatics Institute.
  • Kröse, B. J. A., Vlassis, N., Bunschoten, R., & Motomura, Y. (2000). Feature selection for appearance-based robot localization. In Proceedings RWC 2000, Real World Computing Symposium Real World Computing Partnership.
  • Kröse, B. J. A. (2000). An efficient representation of the robot's environment. In E. Pagello, F. Groen, T. Arai, R. Dillmann, & A. Stentz (Eds.), Proceedings Intelligent Autonomous Systems 6 (pp. 589-596). IOS Press.
  • Kröse, B. J. A., van den Bogaard, R., & Hietbrink, N. (2000). Programming robots is fun: Robocup Jr. 2000. In V. D. Bosch, & O. Weigand (Eds.), Proceedings of the Twelfth Belgium-Netherlands Al Conference BNAIC' 00 (pp. 29-36)
  • Portegies Zwart, J., & Kröse, B. J. A. (2000). Constraint mixture modeling of intrinsically low-dimensional distributions. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alqu' ezar, A. K. Jain, & J. Kittler (Eds.), 15th International Conference on Pattern Recognition, Volume 2: Pattern Recognition and Neural Networks (Vol. 2, pp. 610-613). IEEE.
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