78,958 research outputs found

    Self-Organizing Maps Algorithm for Parton Distribution Functions Extraction

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    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

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    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

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    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

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    Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) 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
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