1,745 research outputs found
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
Neuroengineering of Clustering Algorithms
Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv
Network-based brain computer interfaces: principles and applications
Brain-computer interfaces (BCIs) make possible to interact with the external
environment by decoding the mental intention of individuals. BCIs can therefore
be used to address basic neuroscience questions but also to unlock a variety of
applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In
general, BCI usability critically depends on the ability to comprehensively
characterize brain functioning and correctly identify the user s mental state.
To this end, much of the efforts have focused on improving the classification
algorithms taking into account localized brain activities as input features.
Despite considerable improvement BCI performance is still unstable and, as a
matter of fact, current features represent oversimplified descriptors of brain
functioning. In the last decade, growing evidence has shown that the brain
works as a networked system composed of multiple specialized and spatially
distributed areas that dynamically integrate information. While more complex,
looking at how remote brain regions functionally interact represents a grounded
alternative to better describe brain functioning. Thanks to recent advances in
network science, i.e. a modern field that draws on graph theory, statistical
mechanics, data mining and inferential modelling, scientists have now powerful
means to characterize complex brain networks derived from neuroimaging data.
Notably, summary features can be extracted from these networks to
quantitatively measure specific organizational properties across a variety of
topological scales. In this topical review, we aim to provide the
state-of-the-art supporting the development of a network theoretic approach as
a promising tool for understanding BCIs and improve usability
Graph ambiguity
In this paper, we propose a rigorous way to define the concept of ambiguity in the domain of graphs. In past studies, the classical definition of ambiguity has been derived starting from fuzzy set and fuzzy information theories. Our aim is to show that also in the domain of the graphs it is possible to derive a formulation able to capture the same semantic and mathematical concept. To strengthen the theoretical results, we discuss the application of the graph ambiguity concept to the graph classification setting, conceiving a new kind of inexact graph matching procedure. The results prove that the graph ambiguity concept is a characterizing and discriminative property of graphs. (C) 2013 Elsevier B.V. All rights reserved
Multi-label Classification via Adaptive Resonance Theory-based Clustering
This paper proposes a multi-label classification algorithm capable of
continual learning by applying an Adaptive Resonance Theory (ART)-based
clustering algorithm and the Bayesian approach for label probability
computation. The ART-based clustering algorithm adaptively and continually
generates prototype nodes corresponding to given data, and the generated nodes
are used as classifiers. The label probability computation independently counts
the number of label appearances for each class and calculates the Bayesian
probabilities. Thus, the label probability computation can cope with an
increase in the number of labels. Experimental results with synthetic and
real-world multi-label datasets show that the proposed algorithm has
competitive classification performance to other well-known algorithms while
realizing continual learning
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