Abstract
Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. We proposed a novel pruning method for efficient classification and we call this model as self-organizing neural grove (SONG). In this paper, we investigate SONG’s incremental learning performance.
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Inoue, H. (2010). Incremental Learning Using Self-Organizing Neural Grove. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_77
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DOI: https://doi.org/10.1007/978-3-642-15825-4_77
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