Diversity among the base classifiers is deemed to be important when constructing a
classifier ensemble. Numerous algorithms have been proposed to construct a good …

We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG)
framework, called Robust Growing Neural Gas (RGNG) network. By incorporating several …

Application of boosting to both two-class and multi-class classification problems are studied.
Five real chemical data sets are used. Each data is randomly divided into two subsets, one …

This paper proposes a hybrid neural network model using a possible combination of
different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel …

Neurons are electrically active structures determined by the evolution of ion-specific pumps
and channels that allow the transfer of charges under the influence of electric fields and …

Prototype based learning algorithms, such as Kohonen's learning vector quantization (LVQ)
algorithm and its variants, offer the simple and intuitive model while excellent generalization …

Best clustering analysis should be resisting the presence of outliers and be less sensitive to
initialization as well as the input sequence ordering. This chapter compares the performance …

Актуальность темы исследования. В настоящее время в различных сферах
человеческой деятельности для повышения эффективности оценки состояния …

Two comprehensive lists of articles on the Self-Organizing Map (SOM) have been published
earlier in the Neural Computing Surveys. They contain references to scientific papers that …

Clustering data based on a measure of similarity (or dissimilarity) is a critical step in scientific
data analysis and especially in current bioinformatics field. A typical example is, with the …