Abstract
This paper proposes a new vector distance evaluation function for vector classifications. The proposed distance evaluation function is the weighted sum of the differences between vector elements. The weight values are determined according to whether the input vector element is in the neighborhood of the prototype vector element or not. If the element is not within the neighborhood, then the weight is selected so that the distance measure is less significant The proposed distance measure is applied to a hardware vector classifier system and its feasibility is verified by simulations and circuit size evaluation. These simulations and evaluations reveal that the performance of the classifier with the proposed method is better than that of the Manhattan distance classifier and slightly inferior to Gaussian classifier. While providing respectable performance on the classification, the evaluation function can be easily implemented in hardware.
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References
Kawahara, K., Shibata, T.: A New Distance Measure Employing Element-Significance Factor for Robust Image Classification. In: Proc. EUSIPCO 2005 (September 2005)
Lipman, A., Ynag, W.: VLSI hardware for example-based learning. IEEE Trans. VLSI Syst. 5, 320–328 (1997)
Rovetta, S., Zunino, R.: Efficient training of neural gas vector quantizers with analog circuit implementation. IEEE Trans. Circuits Syst. II 46, 688–698 (1999)
Bracco, M., Ridella, S., Zunino, R.: Digital Implementation of Hierarchical Vector Quantization. IEEE Trans. Neural Networks 14(5), 1072–1083 (2003)
Moritake, Y., Hikawa, H.: Category Recognition System Using Two Ultrasonic Sensors and Combinational Logic Circuit (Japanese). IEICE Transactions on Fundamentals J87-A(7), 890–898 (2004)
University of California at Irvine web site, http://wwww.ics.uci.edu/~mlearn/MLRepository.html
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2), 170–188 (1936)
Matsubara, S., Hikawa, H.: Hardware Friendly Vector Quantization Algorithm. In: Proc. IEEE ISCAS 2005, pp. 3623–3626 (2005)
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Hikawa, H., Kugimiya, K. (2008). A New Hardware Friendly Vector Distance Evaluation Function for Vector Classifiers. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_15
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DOI: https://doi.org/10.1007/978-3-540-69162-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
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