3,266 research outputs found
Heterogeneous Kohonen networks
A large number of practical problems involves elements that are described as a mixture of qualitative and quantitative infomation, and whose description is probably incomplete. The self-organizing map is an effective tool for visualization of high-dimensional continuous data. In this work, we extend the network and training algorithm to cope with heterogeneous information, as well as missing values. The classification performance on a collection of benchmarking data sets is compared in different configurations. Various visualization methods are suggested to aid users interpret post-training results.Peer ReviewedPostprint (author's final draft
Optimising the topology of complex neural networks
In this paper, we study instances of complex neural networks, i.e. neural
netwo rks with complex topologies. We use Self-Organizing Map neural networks
whose n eighbourhood relationships are defined by a complex network, to
classify handwr itten digits. We show that topology has a small impact on
performance and robus tness to neuron failures, at least at long learning
times. Performance may howe ver be increased (by almost 10%) by artificial
evolution of the network topo logy. In our experimental conditions, the evolved
networks are more random than their parents, but display a more heterogeneous
degree distribution
Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics
Physics analysis in astroparticle experiments requires the capability of
recognizing new phenomena; in order to establish what is new, it is important
to develop tools for automatic classification, able to compare the final result
with data from different detectors. A typical example is the problem of Gamma
Ray Burst detection, classification, and possible association to known sources:
for this task physicists will need in the next years tools to associate data
from optical databases, from satellite experiments (EGRET, GLAST), and from
Cherenkov telescopes (MAGIC, HESS, CANGAROO, VERITAS)
Artificial neural network-statistical approach for PET volume analysis and classification
Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund
Dynamical Equilibrium, trajectories study in an economical system. The case of the labor market
The paper deals with the study of labor market dynamics, and aims to
characterize its equilibriums and possible trajectories. The theoretical
background is the theory of the segmented labor market. The main idea is that
this theory is well adapted to interpret the observed trajectories, due to the
heterogeneity of the work situations.Comment: accepted to the WSOM 2007 Conference (Bielefield
Multidimensional Urban Segregation - Toward A Neural Network Measure
We introduce a multidimensional, neural-network approach to reveal and
measure urban segregation phenomena, based on the Self-Organizing Map algorithm
(SOM). The multidimensionality of SOM allows one to apprehend a large number of
variables simultaneously, defined on census or other types of statistical
blocks, and to perform clustering along them. Levels of segregation are then
measured through correlations between distances on the neural network and
distances on the actual geographical map. Further, the stochasticity of SOM
enables one to quantify levels of heterogeneity across census blocks. We
illustrate this new method on data available for the city of Paris.Comment: NCAA S.I. WSOM+ 201
Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works
In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers
- …