The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

Human participation plays an essential role in most decisions when analyzing data. The
huge storage capacity and computational power of computers cannot replace the human …

Estimation and modelling problems as they arise in many data analysis areas often turn out
to be unstable and/or intractable by standard numerical methods. Such problems frequently …

Multidimensional scaling (MDS) is a widely used technique for map** data from a high-
dimensional to a lower-dimensional space and for visualizing data. Recently, a new method …

The most classical visualization methods, including multidimensional scaling and its
particular case–Sammon's map**, encounter difficulties when analyzing large data sets …

Implementing energy-efficient solutions in a built environment is important for reaching
international energy reduction targets. For advanced energy efficiency-related solutions …

In this paper, the quality of quantization and visualization of vectors, obtained by vector
quantization methods (self-organizing map and neural gas), is investigated. A …

The problem of visual presentation of multidimensional data is discussed. The projection
methods for dimension reduction are reviewed. The chapter deals with the artificial neural …

In the article, the influence of neighboring functions and learning rates on self-organizing
maps (SOM) has been investigated. The target of a self-organizing map is data clustering …

In 2003, a health IT programme for clinical decision support started in Lithuania. An initial
goal was to create databases for ophthalmology images and to develop processing …