Clustering is a key data mining task. This is the problem of partitioning a set of observations
into clusters such that the intra-cluster observations are similar and the inter-cluster …

Clustering data streams is becoming the most efficient way to cluster a massive dataset. This
task requires a process capable of partitioning observations continuously with restrictions of …

This work presents an adaptive framework for building soft-sensors based on radial basis
function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in …

Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning,
in which the data appear in a sequential manner with a mixture of both labeled and …

This paper proposes a topological clustering algorithm by integrating topological structure
and information theoretic learning, ie, correntropy, into adaptive resonance theory (ART) …

This paper attempts to solve the typical problems of self-organizing growing network models,
ie (a) an influence of the order of input data on the self-organizing ability,(b) an instability to …

This paper introduces, describes, and evaluates real-time models of affective states of
individual learners interacting with Intelligent Tutoring Systems. Computer-based instructors …

Решается задача локализации человека в кадре видеопотока с помощью алгоритма
расширяющегося нейронного газа и признакового описания на основе гистограмм …

The study of brain lesions can benefit from a clear identification of transitions between
healthy and pathological tissues, through the analysis of brain imaging data. Current signal …

The dilemma between stability and plasticity is crucial in machine learning, especially when
non-stationary input distributions are considered. This issue can be addressed by continual …