Abstract
Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computational cost and we called this model as self-organizing neural grove (SONG). In this paper, we investigate a performance of incremental learning using SONG for a large scale classification problem. The results show that the SONG can ensure rapid and efficient incremental learning.
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Inoue, H., Narihisa, H. (2008). Efficient Incremental Learning Using Self-Organizing Neural Grove. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_79
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DOI: https://doi.org/10.1007/978-3-540-69158-7_79
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