Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

Support vector machines (SVMs) are one of the most popular and powerful machine
learning techniques, but suffer from a significant drawback of the high time and memory …

Real life data often suffer from non-informative objects—outliers. These are objects that are
not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper …

In data sets some attributes may have higher or lower importance. One of the tools used for
data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy …

Artificial intelligence is often inspired by biological solutions. Prototypes are among these
inspirations. Humans often describe complex entities by comparing them to previously …

Neuro-fuzzy systems have been proved to be an efficient tool for modelling real life systems.
They are precise and have ability to generalise knowledge from presented data. Neuro …

Support vector machines (SVMs) are a widely-used machine learning technique, but they
suffer from a significant drawback of high time and memory training complexity, which …

Support vector machines (SVMs) have been found highly helpful in solving numerous
pattern recognition tasks. Although it is challenging to train SVMs from large data sets, this …

Fuzzy models are known for their ability of precise data approximation. They can be used in
automatic control as controllers. One of the techniques is based on inversion of fuzzy …

Support vector machines (SVMs) are a well-established classifier, already applied in a
variety of pattern recognition tasks. However, they suffer from several drawbacks—selecting …