Abstract
In this work, a general framework for developing learning rules with an added term (perturbation term) is presented. Many learning rules commonly cited in the specialized literature can be derived from this general framework. This framework allows us to introduce some knowledge about vector quantization (as an optimization problem) in the distortion function in order to derive a new learning rule that uses that information to avoid certain local minima of the distortion function, leading to better performance than classical models. Computational experiments in image compression show that our proposed rule, derived from this general framework, can achieve better results than simple competitive learning and other models, with codebooks of less distortion.
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Mérida-Casermeiro, E., López-Rodríguez, D., Galán-Marín, G., Ortiz-de-Lazcano-Lobato, J.M. (2007). Improved Production of Competitive Learning Rules with an Additional Term for Vector Quantization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_51
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DOI: https://doi.org/10.1007/978-3-540-71618-1_51
Publisher Name: Springer, Berlin, Heidelberg
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