Neural Network Classifier




List of Publications(with review)

  1. M. Nakao, T. Kanaoka, Y. Hamamoto and S. Tomita: ``A Note on the Order of Higher-Order Neural Network for Pattern Recognition'', IEICE Trans.(D-II), Vol.J76-D-II, No.3, pp. 804-806 (1993). (in Japanese)

  2. Y. Hamamoto, S. Uchimura, T. Kanaoka and S. Tomita: ``Evaluation of artificial neural network classifiers in small sample size situations'', Proc. of Int. Joint Conf. Neural Networks, Nagoya, pp. 1731-1735 (Oct. 1993).

  3. Y. Hamamoto, S. Uchimura and S. Tomita: ``Small training sample size problems in artificial neural network classifier design'', Current Topics in Pattern Recognition Research, 1, pp. 1-5 (1994).

  4. K. Kobara, T. Kanaoka, Y. Hamamoto, S. Tomita and K. Munechika: ``Use of gradated patterns in an associative neural memory for invariant pattern recognition'', Int. J. of Pattern Recognition and Artificial Intelligence, 8, 2, pp. 595-607 (1994).

  5. Y. Hamamoto, Y. Mitani and S. Tomita: ``On the effect of the noise injection in small training sample size situations'', Proc. of Int. Conf. Neural Information Processing, Seoul, pp. 626-628 (Oct. 1994).

  6. Y. Mitani, Y. Hamamoto and S. Tomita: ``Use of bootstrap samples in designing artificial neural network classifiers'', Proc. of the IEEE Int. Conf. Neural Networks, Perth, pp. 2103-2106 (Nov. 1995).

  7. S. Uchimura, Y. Hamamoto and S. Tomita: ``On the effect of the nonlinearity of the sigmoid function in artificial neural network classifiers'', Proc. of the IEEE Int. Conf. Neural Networks, Perth, pp. 281-284 (Nov. 1995).

  8. S. Uchimura, Y. Hamamoto and S. Tomita: ``Effects of the sample size in artificial neural network classifier design'', Proc. of the IEEE Int. Conf. Neural Networks, Perth, pp. 2126-2129 (Nov. 1995).

  9. Y. Mitani, Y. Hamamoto and S. Tomita: ``Back-propagation learning with bootstrap samples'', Proc. the Second Asian Conf. Computer Vision, Singapore, Vol. II, pp. 90-93 (Dec. 1995).

  10. Y. Hamamoto, T. Hase, S. Nakai and S. Tomita: ``Comparison of pruning algorithms in neural networks'', Proc. of 5th Conf. of Int. Federation of Classification Societies, Kobe, Vol. 1, pp. 81-84 (March 1996).

  11. Y. Mitani, Y. Hamamoto and S. Tomita: ``A consideration on the bootstrapping in artificial neural network classifier design'', Proc. of 5th Conf. of Int. Federation of Classification Societies, Kobe, Vol. 2, pp.271-274 (March 1996).

  12. Y. Hamamoto, S. Uchimura and S. Tomita: ``On the behavior of artificial neural network classifiers in high-dimensional spaces'', IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 18, No. 5, pp. 571-574 (1996).

  13. Y. Hamamoto, T. Hase, Y. Mitani and S. Tomita: ``A note on the generalization error in neural networks'', Proc. of 9th Int. Conf. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Fukuoka, pp. 559-562 (June 1996).

  14. Y. Hamamoto, Y. Mitani, H. Ishihara, T. Hase and S. Tomita: ``Evaluation of an anti-regularization in neural networks'', Proc. of 13th Int. Conf. Pattern Recognition, Vienna, pp. 205-208 (Aug. 1996).

  15. Y. Mitani, Y. Hamamoto and S. Tomita: ``Evaluation of the noise injection in high dimensions'', Proc. of IAPR Workshop Machine Vision Applications, Tokyo, pp. 107-110 (Nov. 1996).

  16. Y. Hamamoto, T. Hase, S. Nakai and S. Tomita: ``Comparison of pruning algorithms in neural networks'', in Data Science, Classification, and Related Methods, C. Hayashi, Eds., pp. 328-333, Springer (1998).

  17. Y. Mitani, Y. Hamamoto: ``A bootstrap-based learning with the cross validation'', Proc. of Fifth Int. Conf. Neural Information Processing, Kitakyushu, Vol. 1, pp.55-58 (Oct. 1998).

  18. Y. Mitani and Y. Hamamoto: ``A bootstrap technique for artificial neural network classifier design'', IEICE Trans.(D-II), Vol.J82-D-II, No.2, pp. 268-275, 1999. (in Japanese)


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