Abstract
The standard SVM classifier is not adjusted to processing large training set as the computational complexity can reach O(n 3). To overcome this limitation we discuss the idea of reducing the size of the training data by initial preprocessing of the training set using Learning Vector Quantization (LVQ) neural network and then building the SVM model using prototypes returned by the LVQ network. As the LVQ network scales linearly with n, and in contrast to clustering algorithms utilizes label information it seems to be a good choice for initial data compression.
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Blachnik, M. (2015). Reducing Time Complexity of SVM Model by LVQ Data Compression. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_61
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DOI: https://doi.org/10.1007/978-3-319-19324-3_61
Publisher Name: Springer, Cham
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