Abstract
In this paper, we adopt an asymmetric neighborhood function proposed by Aoki and Aoyagi in to a PL-G-SOM to improve the learning performance of the hand shape instruction perspective and learning system. The asymmetric neighborhood function was used in a normal SOM and few applications can be found. The novel PL-G-SOM and its improved version are named as "AGSOM" and “IAGSOM” respectively. The effectiveness of the proposed method was confirmed by the experiments with 8 kinds of instructions.
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References
Aoki, T., Aoyagi, T.: Self-organizing maps with asymmetric neighborhood function. Neural Computation 19, 2515–2535 (2007)
Aoki, T., Ota, K., Kurata, K., Aoyagi, T.: Ordering process of self-organizing maps improved by asymmetric neighborhood function. Cognitive Neurodyamics 3, 9–15 (2009)
Ota, K., Aoki, T., Kurata, K., Aoyagi, T.: Asymmetric neighborhood functions accelerate ordering process of self-organizing maps. Physical Review E 83, 021903 (2011)
Bauer, H.-U., Villmann, T.: Growing a hypercubical output space in a self-organizing feature map. IEEE Transaction on Neural Networks 8(2), 218–226 (1997)
Baum, L.E., Petrie, T.: Statistical inference for finite state Markov chains. The Annals of Mathematical Statistics 37(6), 1554–1563 (1966)
Berglund, E., Sitte, J.: The parameter-less self-organizing map algorithm. IEEE Transaction on Neural Networks 17(2), 305–316 (2006)
Kohonen, T.: Self-Organizing Maps. Series in Information Sciences. Springer, Heidelberg (1995)
Kuremoto, T., Hano, T., Kobayashi, K., Obayashi, M.: For partner robots: A hand instruction learning system using transient-SOM. In: Proceedings of the 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy Systems and Knowledge Discovery (ICNC 2006-FSKD 2006), pp. 403–414 (2006)
Kuremoto, T., Obayashi, M., Kobayashi, K., Feng, L.-B.: Instruction learning systems for partner robots. In: Advances in Robotics-Modeling, Control, and Applications, iConcept, ch. 8 (2012)
Kuremoto, T., Otani, T., Feng, L.-B., Kobayashi, K., Obayashi, M.: A hand image instruction learning system using PL-G-SOM. In: Proceedings of the 12th International Conference on Artificial Intelligence (ICAI 2012), CD-ROM (2012)
Sherrah, S., Gong, S.: Skin Colour Analysis (2001), http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/GONG1/cvOnline-skinColourAnalysis.html
Sutton, S.S., Barto, A.G.: Reinforcement Learning: An Instruction. The MIT Press, London (1998)
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Kuremoto, T., Otani, T., Obayashi, M., Kobayashi, K., Mabu, S. (2014). A Hand Shape Instruction Recognition and Learning System Using Growing SOM with Asymmetric Neighborhood Function. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_29
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DOI: https://doi.org/10.1007/978-3-319-09333-8_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
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