— In this paper we present information about our experiment with a social network of students from one school class. Each pupil has characterized his/her relationships to all sch...
In this paper, we present a local, adaptive optimization scheme for adjusting the number of clusters in fuzzy C-means clustering. This method is especially motivated by online app...
— Reinforcement learning (RL) is a learning control paradigm that provides well-understood algorithms with good convergence and consistency properties. Unfortunately, these algor...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
— Fuzzy Description Logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. It is well known that the choice of the fuzzy operat...
— Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzyvalued data. These algorithms are efficient when the observation e...
In this work an improved scheme for eliminating impulsive noise of varying strengths from corrupted images is proposed. A neural network is employed to classify the corrupted and n...
— Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction da...
— Humans can control MIMO (Multiple-Input Multiple-Output) objects appropriately using knowledge of the MIMO object, which can be referred to as human MIMO control knowledge. An ...
— One of the main advantages of fuzzy modeling is the ability to yield interpretable results. Amongst these modeling methods, the OLS algorithm is a mathematically robust techniq...
— The algebra of truth values of type-2 fuzzy sets contains isomorphic copies of the algebra of truth values of type1 fuzzy sets and the algebra of truth values of interval-value...