Abstract
Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology for data in categorical nonvector spaces. Experiments on UCI data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to handle data with categorical values.
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Chen, N., Marques, N.C. (2010). Extending Learning Vector Quantization for Classifying Data with Categorical Values. In: Filipe, J., Fred, A., Sharp, B. (eds) Agents and Artificial Intelligence. ICAART 2009. Communications in Computer and Information Science, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11819-7_10
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DOI: https://doi.org/10.1007/978-3-642-11819-7_10
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