Classification of the text documents into a predefined set of classes is considered to be an
important task for natural language processing applications. There is usually a tradeoff …

One of the several benefits of text classification is to automatically assign document in
predefined category. Researchers using LVQ algorithm in English and Persian [1, 2] and …

Automatic text classification is an important task for many natural language processing
applications. This paper presents a neural approach to develop a text classifier based on the …

Humans can recognize different types of written languages by their grammars and
vocabularies. However, computers see everything as numbers. We present a computational …

As a simple classification method KNN has been widely applied in text classification. There
are two problems in KNN-based text classification: the large computation load and the …

Classifiers derived by learning vector quantization (LVQ) have well-defined decision regions
that can be combined to construct a more accurate classifier. A given point may be included …

In this paper, the criterion for training reference vectors is formulated in which the reference
vectors are modified by the input vectors closer to decision boundaries. The authors present …

Learning vector quantization (LVQ), developed by T. Kohonen (1989), is a neural network
based method to find a good set of reference vectors to be stored as a nearest neighbor …

When compared with speech technologies in speech processing, automatic language
identification is a relatively new yet difficult problem. In this paper, two language …

Many machine learning methods have been proposed for text categorization, but most
research has applied them to English documents. Vietnamese is a different language with …