A surge in the availability of data from multiple sources and modalities is correlated with
advances in how to obtain, compress, store, transfer, and process large amounts of complex …

Subspace clustering is the task of identifying clusters in subspaces of the input dimensions
of a given dataset. Noisy data in certain attributes cause difficulties for traditional clustering …

Distance-based neural network clustering requires the intrinsic assumption that a particular
neuron in the network represents a cluster centroid. However, not all these neurons can …

Many works in the recent literature introduce semantic mapping methods that use CNNs
(Convolutional Neural Networks) to recognize semantic properties in images. The types of …

Clustering complexity increases with the number of categories and sub-categories and with
data dimensionality. In this case, the distance metrics lose discrimination power with the …

This article proposes a biologically inspired neurocomputational architecture which learns
associations between words and referents in different contexts, considering evidence …

Recently, it has been shown that under a broad set of conditions, the commonly used
distance functions will become unstable in high-dimensional data space; ie, the distance to …

Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously
unknown and appear with a given frequency in time series. Another problem strongly related …

The advancement of robotics may produce a positive impact on several aspects of our
society. However, in order for robotic agents to assist humans in a variety of everyday ac …

Title: Extending Self-organizing Maps with Ranking Awareness Author: Kyung Won Park
Department: Department of Software Engineering Supervisor: Mgr. Ladislav Peska, Ph. D …