Comparative Analysis of Restricted Boltzmann Machine Models for Image Classification

  • Christine Dewi
  • Rung-Ching ChenEmail author
  • Hendry
  • Hsiu-Te Hung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)


Many applications for Restricted Boltzmann Machines (RBM) have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer. We compare the classification performance of regular RBM use RBM() function, classification RBM use stackRBM() function and Deep Belief Network (DBN) use DBN() function with different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compare to regular RBM.


Classification comparison DBN RBM Stack-RBM 



This paper is supported by Ministry of Science and Technology, Taiwan. The Nos are MOST-107-2221-E-324-018-MY2 and MOST-106-2218-E-324-002, Taiwan.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information ManagementChaoyang University of TechnologyTaichungTaiwan, R.O.C.
  2. 2.Faculty of Information TechnologySatya Wacana Christian UniversitySalatigaIndonesia

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