78,958 research outputs found
Self-Organizing Maps Algorithm for Parton Distribution Functions Extraction
We describe a new method to extract parton distribution functions from hard
scattering processes based on Self-Organizing Maps. The extension to a larger,
and more complex class of soft matrix elements, including generalized parton
distributions is also discussed.Comment: 6 pages, 3 figures, to be published in the proceedings of ACAT 2011,
14th International Workshop on Advanced Computing and Analysis Techniques in
Physics Researc
Self-Organizing Maps and Parton Distributions Functions
We present a new method to extract parton distribution functions from high
energy experimental data based on a specific type of neural networks, the
Self-Organizing Maps. We illustrate the features of our new procedure that are
particularly useful for an anaysis directed at extracting generalized parton
distributions from data. We show quantitative results of our initial analysis
of the parton distribution functions from inclusive deep inelastic scattering.Comment: 8 pages, 4 figures, to appear in the proceedings of "Workshop on
Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th
-21st, 201
Advances in Self Organising Maps
The Self-Organizing Map (SOM) with its related extensions is the most popular
artificial neural algorithm for use in unsupervised learning, clustering,
classification and data visualization. Over 5,000 publications have been
reported in the open literature, and many commercial projects employ the SOM as
a tool for solving hard real-world problems. Each two years, the "Workshop on
Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM
series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has
been successfully organized in 1997 and 1999 by the Helsinki University of
Technology, in 2001 by the University of Lincolnshire and Humberside, and in
2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I
Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in
Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari
Improving novelty in streaming recommendation using a context model
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â In recent years there has been an increasing research interest
in novelty/diversity detection in Information Retrieval and
in Recommendation Systems. We propose a model that increases
the novelty of recommendations using a context user
profile that was created automatically using self-organizing
maps. Our system was evaluated on the Reuters Corpus
Volume 1 and our experiments show that filtering the recommended
items using a novelty score derived from the contextbased
user profile provides better search results in terms of
novel information that is presented to the user.This work was supported by the Ministerio de Educaci on y
Ciencia under the grant N. TIN2011-28538-C02, Novelty, di-
versity, context and time: newdimensions in next-generation
information retrieval and recommender systems
A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM¿+¿2019
Postprint (author's final draft
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