@inproceedings{13a33f563ef749748774766388fd08fc,
title = "Extending learning vector quantization for classifying data with categorical values",
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.",
keywords = "Batch learning, Categorical value, Learning vector quantization, Self-organizing map",
author = "Ning Chen and Marques, {Nuno C.}",
year = "2010",
doi = "10.1007/978-3-642-11819-7_10",
language = "English",
isbn = "3642118186",
series = "Communications in Computer and Information Science",
pages = "124--136",
booktitle = "Agents and Artificial Intelligence - International Conference, ICAART 2009, Revised Selected Papers",
note = "1st International Conference on Agents and Artificial Intelligence, ICAART 2009 ; Conference date: 19-01-2009 Through 21-01-2009",
}