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  • Lindenbaum M and Ben-David S. (1999). VC-Dimension Analysis of Object Recognition Tasks. Journal of Mathematical Imaging and Vision. 10:1. (27-49). Online publication date: 1-Jan-1999.

    https://doi.org/10.1023/A:1008314532315

  • Ben-David S and Lindenbaum M. (1998). Localization vs. Identification of Semi-Algebraic Sets. Machine Language. 32:3. (207-224). Online publication date: 1-Sep-1998.

    https://doi.org/10.1023/A:1007447530834

  • Lindenbaum M. (1997). An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19:11. (1251-1264). Online publication date: 1-Nov-1997.

    https://doi.org/10.1109/34.632984

  • Kowalczyk A. (2018). Estimates of storage capacity of multilayer perceptron with threshold logic hidden units. Neural Networks. 10:9. (1417-1433). Online publication date: 1-Nov-1997.

    https://doi.org/10.1016/S0893-6080(97)00009-9

  • Ben-David S and Lindenbaum M. (2019). Learning Distributions by Their Density Levels. Journal of Computer and System Sciences. 55:1. (171-182). Online publication date: 1-Aug-1997.

    https://doi.org/10.1006/jcss.1997.1507

  • Rudshtein A and Lindenbaum M. (1996). Quantifying the reliability of feature-based object recognition Proceedings of 13th International Conference on Pattern Recognition. 10.1109/ICPR.1996.545987. 0-8186-7282-X. (35-39 vol.1).

    http://ieeexplore.ieee.org/document/545987/

  • Lindenbaum M. (1996). An integrated model for evaluating the amount of data required for reliable recognition. Recent Developments in Computer Vision. 10.1007/3-540-60793-5_99. (457-466).

    http://link.springer.com/10.1007/3-540-60793-5_99

  • Ben-David S and Lindenbaum M. (1995). Learning distributions by their density levels — A paradigm for learning without a teacher. Computational Learning Theory. 10.1007/3-540-59119-2_168. (53-68).

    http://link.springer.com/10.1007/3-540-59119-2_168

  • Lindenbaum M and Ben-David S. Applying VC-dimension analysis to 3D object recognition from perspective projections. Proceedings of the Twelfth AAAI National Conference on Artificial Intelligence. (985-990).

    /doi/10.5555/2891730.2891882

  • Lindenbaum M and Ben-David S. (1994). Applying VC-dimension analysis to object recognition. Computer Vision — ECCV '94. 10.1007/3-540-57956-7_29. (237-250).

    http://link.springer.com/10.1007/3-540-57956-7_29

  • Kowalczyk A. Counting function theorem for multi-layer networks. Proceedings of the 7th International Conference on Neural Information Processing Systems. (375-382).

    /doi/10.5555/2987189.2987237