Initialization Method of Reference Vectors for Improvement of Recognition Accuracy in LVQ
Initial values of reference vectors have significant influence on recognition accuracy in LVQ. There are several existing techniques, such as SOM and k-means, for setting initial values of reference vectors, each of which has provided some positive results. However, those results are not sufficient for the improvement of recognition accuracy. This study proposes an ACO-used method for initializing reference vectors with an aim to achieve recognition accuracy higher than those obtained through conventional methods. Moreover, we will demonstrate the effectiveness of the proposed method by applying it to the wine data and English vowel data and comparing its results with those of conventional methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057751Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 923
 Abhishek Bansal and G. N. Pillai, "High Impedance Fault Detection using LVQ Neural Networks", International Journal of Computer, Information, and Systems Science, and Engineering 1;3 www.waset.org Summer 2007.
 Yuji Mizuno, Hiroshi Mabuchi, Goutam Chakraborty, Masafumi Matsuhara, "Clustering of EEG Data using Maximum Entropy Method and LVQ" INTERNATIONAL JOURNAL OF COMPUTERS, Issue 4, Volume 4, pp.193-200, 2010.
 T. Kohonen, "Self-Organizing Maps", pringer Verlag, 1995.
 Atsushi SATO, Keiji YAMADA, "A Formulation of Learning Vector Quantization Using a New Misclassification Measure", 14th International Conference on Pattern Recognition (ICPR-98) - Volume 1, pp.322-pp325, 1998.
 T. Kohonen, "The Self-Organizing Map", PROCEEDINGS OF THE IEEE, VOL.78, NO.9, pp1464-1480, SEPTEMBER 1990.
 John S Baras and Anthony La Vigna, "Convergence of Kohonen-s Learning Vector Quantization", IJCNN, Vol.3, pp.17-20, 1990.
 Kitajima N, "A new method for initializing reference vectors in LVQ", Proceedings of IEEE International Conference on Neural Networks, Vol.5, pp.2775-2779, 1995.
 Marco Dorigo, Luca Maria Gambardella, "Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem", IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 1, NO. 1, pp.53-66, 1997.
 B. Bullnheimer, R. F. Hartl and C. Strauss, "An improved Ant System algorithm for the vehicle routing problem", Technical Report POM10/97, Vienna, Austria: University of Vienna, Institute of Management Science. To appear in Dawid, Feichtinger, & Hartl (Eds.), Annals of Operations Research: Nonlinear Economic Dynamics and Control, 1997.
 B. Bullnheimer, R. F. Hartl and C. Strauss, "A new rank-based version of the ant system", a computational study, Technical Report POM10/97, Institure of Management Science, University of Vienna, 1997.
 Thomas St┬¿utzle, Holger H. Hoos, "MAX-MIN Ant System", Future Generation Computer Systems, Vol.16, No.8, pp.899-914, 2000.
 M. Dorigo and G. D. Caro, "Ant Algorithms for Discrete Optimization", Artificial Life, vol.5, No.2, pp. 137-172, MIT Press(1999).
 Marco Dorigo, Vittorio Maniezzo, Alberto Colorni, "The Ant System: Optimization by a colony of cooperating agents", IIEEE Transactions on Systems, Man, and Cybernetics.Part B, Vol.26, No.1, pp.1-13, 1996.
 V.Maniezzo, "Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem" Technical Report CSR 98-1, C. L. In scenze dell-Informazione, Universita di Bologna, sede di Cesena,1998.
 E.-G. Talbi, O. Roux, C. Fonlupt, D. Robillard, "Parallel Ant Colonies for the quadratic assignment problem", Future Generation Computer Systems 17 pp.441-449, 2001.
 Shigeyoshi Tsutsui, "Cunning Ant System for Quadratic Assignment Problem with Local Search and Parallelization", Proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, pp. 269-278, Springer-Velag , December 18-22, 2007, Indian Statistical Institute, Kolkata, 2007.12. Springer LNCS 4815, ISBN: 978- 3-540-77045-9.
 Payam Refaeilzadeh, Lei Tang, Huan Liu, "Cross Validation", In Encyclopedia of Database Systems, Editors: M. Tamer ┬¿Ozsu and Ling Liu. Springer, 2009.
 C. L. Blake, C. J. Merz,UCI Repositoy Machine Learning Databases, Irvine, University of California, http://archive.ics.uci.edu/ml/, 2010/12.