1,779 research outputs found
Magnification Control in Self-Organizing Maps and Neural Gas
We consider different ways to control the magnification in self-organizing
maps (SOM) and neural gas (NG). Starting from early approaches of magnification
control in vector quantization, we then concentrate on different approaches for
SOM and NG. We show that three structurally similar approaches can be applied
to both algorithms: localized learning, concave-convex learning, and winner
relaxing learning. Thereby, the approach of concave-convex learning in SOM is
extended to a more general description, whereas the concave-convex learning for
NG is new. In general, the control mechanisms generate only slightly different
behavior comparing both neural algorithms. However, we emphasize that the NG
results are valid for any data dimension, whereas in the SOM case the results
hold only for the one-dimensional case.Comment: 24 pages, 4 figure
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Analysis of Professional Trajectories using Disconnected Self-Organizing Maps
In this paper we address an important economic question. Is there, as
mainstream economic theory asserts it, an homogeneous labor market with
mechanisms which govern supply and demand for work, producing an equilibrium
with its remarkable properties? Using the Panel Study of Income Dynamics (PSID)
collected on the period 1984-2003, we study the situations of American workers
with respect to employment. The data include all heads of household (men or
women) as well as the partners who are on the labor market, working or not.
They are extracted from the complete survey and we compute a few relevant
features which characterize the worker's situations. To perform this analysis,
we suggest using a Self-Organizing Map (SOM, Kohonen algorithm) with specific
structure based on planar graphs, with disconnected components (called D-SOM),
especially interesting for clustering. We compare the results to those obtained
with a classical SOM grid and a star-shaped map (called SOS). Each component of
D-SOM takes the form of a string and corresponds to an organized cluster. From
this clustering, we study the trajectories of the individuals among the classes
by using the transition probability matrices for each period and the
corresponding stationary distributions. As a matter of fact, we find clear
evidence of heterogeneous parts, each one with high homo-geneity, representing
situations well identified in terms of activity and wage levels and in degree
of stability in the workplace. These results and their interpretation in
economic terms contribute to the debate about flexibility which is commonly
seen as a way to obtain a better level of equilibrium on the labor market
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
Multistrategy Self-Organizing Map Learning for Classification Problems
Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test
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