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Author (up) Adriana Romero; Carlo Gatta edit   pdf
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  Title Do We Really Need All These Neurons? Type Conference Article
  Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 7887 Issue Pages 460--467  
  Keywords Retricted Boltzmann Machine; hidden units; unsupervised learning; classification  
  Abstract Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch.  
  Address Madeira; Portugal; June 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-38627-5 Medium  
  Area Expedition Conference IbPRIA  
  Notes MILAB; 600.046 Approved no  
  Call Number Admin @ si @ RoG2013 Serial 2311  
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