1,961 research outputs found
Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map
Whileas the Kohonen Self Organizing Map shows an asymptotic level density
following a power law with a magnification exponent 2/3, it would be desired to
have an exponent 1 in order to provide optimal mapping in the sense of
information theory. In this paper, we study analytically and numerically the
magnification behaviour of the Elastic Net algorithm as a model for
self-organizing feature maps. In contrast to the Kohonen map the Elastic Net
shows no power law, but for onedimensional maps nevertheless the density
follows an universal magnification law, i.e. depends on the local stimulus
density only and is independent on position and decouples from the stimulus
density at other positions.Comment: 8 pages, 10 figures. Link to publisher under
http://link.springer.de/link/service/series/0558/bibs/2415/24150939.ht
Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
This paper assumes the hypothesis that human learning is perception based,
and consequently, the learning process and perceptions should not be
represented and investigated independently or modeled in different simulation
spaces. In order to keep the analogy between the artificial and human learning,
the former is assumed here as being based on the artificial perception. Hence,
instead of choosing to apply or develop a Computational Theory of (human)
Perceptions, we choose to mirror the human perceptions in a numeric
(computational) space as artificial perceptions and to analyze the
interdependence between artificial learning and artificial perception in the
same numeric space, using one of the simplest tools of Artificial Intelligence
and Soft Computing, namely the perceptrons. As practical applications, we
choose to work around two examples: Optical Character Recognition and Iris
Recognition. In both cases a simple Turing test shows that artificial
perceptions of the difference between two characters and between two irides are
fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24
Aug 201
Intelligent search for distributed information sources using heterogeneous neural networks
As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources
Generation of folk song melodies using Bayes transforms
The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models
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)
Mixing and condensation in a wet granular medium
We have studied the effect of small amounts of added liquid on the dynamic
behavior of a granular system consisting of a mixture of glass beads of two
different sizes. Segregation of the large beads to the top of the sample is
found to depend in a nontrivial way on the liquid content. A transition to
viscoplastic behavior occurs at a critical liquid content, which depends upon
the bead size. We show that this transition can be interpreted as a
condensation due to the hysteretic liquid bridge forces connecting the beads,
and provide the corresponding phase diagram.Comment: submitted to PR
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Soft topographic map for clustering and classification of bacteria
In this work a new method for clustering and building a
topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different
type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria
class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification
or erroneous annotations in the database
Dynamic clustering of time series with Echo State Networks
In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Comparison between methods for the determination of the primary cosmic ray mass composition from the longitudinal profile of atmospheric cascades
The determination of the primary cosmic ray mass composition from the
longitudinal development of atmospheric cascades is still a debated issue. In
this work we discuss several data analysis methods and show that if the entire
information contained in the longitudinal profile is exploited, reliable
results may be obtained. Among the proposed methods FCC ('Fit of the Cascade
Curve'), MTA ('Multiparametric Topological Analysis') and NNA ('Neural Net
Analysis') with conjugate gradient optimization algorithm give the best
accuracy.Comment: 22 pages, 11 figures, accepted by Astroparticle Physics, minor
misprints and an extra figure remove
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