Improving learning vector quantization using data reduction

Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively

Handwritten Greek Character Recognition with Learning Vector Quantization

This paper presents a handwritten Greek character recognizer. The recognizer is composed of two modules: the first one is a feature extractor, the second one, the classifier, is performed by means of Learning Vector Quantization. The recognizer, tested on a database of more than 28000 handwritten Greek characters, has shown satisfactory performances