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Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications

December 2009 (vol. 31 no. 12)

pp. 2168-2178

[1] N. Arica and F.T.Y. Vural, “A Shape Descriptor Based on Circular Hidden Markov Model,” Proc. 15th Int'l Conf. Pattern Recognition, 2000.
[2] S. Bandyopadhyay and U. Maulik, “Non-Parametric Genetic Clustering: Comparison of Validity Indices,” IEEE Trans. Systems, Man, and Cybernetics, Part C, vol. 31, no. 1, pp. 120-125, Feb. 2001.
[3] Y. Bengio, “Markovian Models for Sequential Data,” Neural Computing Surveys, vol. 2, pp. 129-162, 1999.
[4] A. BrittoJr., “A Two-Stage HMM-Based Method for Recognizing Handwritten Numeral Strings,” PhD thesis, Pontifical Catholic Univ. of Paraná, 2001.
[5] A. Britto, R. Sabourin, F. Bortolozzi, and C.Y. Suen, “Recognition of Handwritten Numeral Strings Using a Two-Stage HMM-Based Method,” Int'l J. Document Analysis and Recognition, vol. 5, no. 2, pp. 102-117, 2003.
[6] G. Brown, J. Wyatt, R. Harris, and X. Yao, “Diversity Creation Methods: A Survey and Categorisation,” Int'l J. Information Fusion, vol. 6, no. 1, pp. 5-20, 2005.
[7] T.G. Dietterich, “Machine Learning for Sequential Data: A Review,” Structural, Syntactic, and Statistical Pattern Recognition, pp. 15-30, Springer-Verlag, 2002.
[8] D. Eppstein, “Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs,” Proc. Ninth ACM-SIAM Symp. Discrete Algorithms, pp. 619-628, 1998.
[9] S. Gunter and H. Bunke, “Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms,” Proc. Eighth Int'l Workshop Frontiers in Handwriting Recognition, pp. 183-188, 2002.
[10] S. Gunter and H. Bunke, “Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition,” Proc. the Third Int'l Workshop Multiple Classifier Systems, pp. 179-188, 2002.
[11] S. Gunter and H. Bunke, “A New Combination Scheme for HMM-Based Classifiers and Its Application to Handwriting Recognition,” Proc. 16th Int'l Conf. Pattern Recognition, vol. 2, pp. 332-337, 2002.
[12] S. Gunter and H. Bunke, “Ensembles of Classifiers for Handwritten Word Recognition,” Int'l J. Document Analysis and Recognition, vol. 5, no. 4, pp. 224-232, 2003.
[13] S. Gunter and H. Bunke, “New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task,” Proc. Fourth Int'l Workshop Multiple Classifier Systems, pp. 326-335, 2003.
[14] S. Gunter and H. Bunke, “Offline Cursive Handwriting Recognition—On the Influence of Training Set and Vocabulary Size in Multiple Classifier Systems,” Proc. 11th Conf. Int'l Graphonomics Soc., 2003.
[15] M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “On Clustering Validation Techniques,” J. Intelligent Information Systems, vol. 17, pp. 2-3, 2001.
[16] M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “Clustering Validity Checking Methods: Part II,” ACM SIGMOD Record, vol. 3, no. 3, pp. 19-27, 2002.
[17] A.H.R. Ko, R. Sabourin, and A. BrittoJr., “A New HMM-Based Ensemble Generation Method for Character Recognition,” Proc. Int'l Workshop Multiple Classifier Systems, pp. 52-61, 2007.
[18] A.H.R. Ko, R. Sabourin, and A. BrittoJr., “Ensemble of HMM Classifiers Based on the Clustering Validity Index for a Handwritten Numeral Recognizer,” Pattern Analysis and Applications J., 2008, doi 10.1007/s10044-007-0094-6.
[19] L.I. Kuncheva, M. Skurichina, and R.P.W. Duin, “An Experimental Study on Diversity for Bagging and Boosting with Linear Classifiers,” Int'l J. Information Fusion, vol. 3, no. 2, pp. 245-258, 2002.
[20] J. Milgram, M. Cheriet, and R. Sabourin, “Estimating Accurate Multi-Class Probabilities with Support Vector Machines,” Proc. Int'l Joint Conf. Neural Networks, pp. 1906-1911, 2005.
[21] L.E.S. Oliveira, R. Sabourin, F. Bortolozzi, and C.Y. Suen, “Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1438-1454, Nov. 2002.
[22] L.E.S. Oliveira and R. Sabourin, “Support Vector Machines for Handwritten Numeral String Recognition,” Proc. Ninth Int'l Workshop Frontiers in Handwriting Recognition, pp. 39-44, Oct. 2004.
[23] M.K. Pakhira, S. Bandyopadhyay, and U. Maulik, “Validity Index for Crisp and Fuzzy Clusters,” Pattern Recognition, vol. 37, no. 3, pp. 487-501, 2004.
[24] L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
[25] L.R. Rabiner and B.H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993.
[26] P. Radtke, T. Wong, and R. Sabourin, “An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization,” Proc. IEEE World Congress on Computational Intelligence—Int'l Joint Conf. Neural Networks, 2006.
[27] D. Ruta and B. Gabrys, “Classifier Selection for Majority Voting,” Int'l J. Information Fusion, pp. 63-81, 2005.
[28] P. Smyth, D. Heckerman, and M.I. Jordan, “Probabilistic Independence Networks for Hidden Markov Probability Models,” Neural Computation, vol. 9, pp. 227-269, 1997.
[29] X. Wang, “Durationally Constrained Training of HMM without Explicit State Durational,” Proc. Inst. of Phonetic Sciences, vol. 18, pp. 111-130, 1994.
[30] X.L. Xie and G. Beni, “A Validity Measure for Fuzzy Clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 841-847, Aug. 1991.