3 research outputs found
Self-organising maps : statistical analysis, treatment and applications.
This thesis presents some substantial theoretical analyses and optimal treatments
of Kohonen's self-organising map (SOM) algorithm, and explores the practical
application potential of the algorithm for vector quantisation, pattern classification,
and image processing. It consists of two major parts. In the first part, the SOM
algorithm is investigated and analysed from a statistical viewpoint. The proof of its
universal convergence for any dimensionality is obtained using a novel and
extended form of the Central Limit Theorem. Its feature space is shown to be an
approximate multivariate Gaussian process, which will eventually converge and
form a mapping, which minimises the mean-square distortion between the feature
and input spaces. The diminishing effect of the initial states and implicit effects of
the learning rate and neighbourhood function on its convergence and ordering are
analysed and discussed. Distinct and meaningful definitions, and associated
measures, of its ordering are presented in relation to map's fault-tolerance. The
SOM algorithm is further enhanced by incorporating a proposed constraint, or
Bayesian modification, in order to achieve optimal vector quantisation or pattern
classification. The second part of this thesis addresses the task of unsupervised
texture-image segmentation by means of SOM networks and model-based
descriptions. A brief review of texture analysis in terms of definitions, perceptions,
and approaches is given. Markov random field model-based approaches are
discussed in detail. Arising from this a hierarchical self-organised segmentation
structure, which consists of a local MRF parameter estimator, a SOM network, and
a simple voting layer, is proposed and is shown, by theoretical analysis and
practical experiment, to achieve a maximum likelihood or maximum a posteriori
segmentation. A fast, simple, but efficient boundary relaxation algorithm is
proposed as a post-processor to further refine the resulting segmentation. The class
number validation problem in a fully unsupervised segmentation is approached by
a classical, simple, and on-line minimum mean-square-error method. Experimental
results indicate that this method is very efficient for texture segmentation
problems. The thesis concludes with some suggestions for further work on SOM
neural networks
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion