Author(s): Passos, Leandro Aparecido ; Cleison Santana, Marcos [UNESP] ; Moreira, Thierry [UNESP] ; Papa, Joao Paulo [UNESP]
Date: 2020
Persistent ID: http://hdl.handle.net/11449/201217
Origin: Oasisbr
Subject(s): Kaniadakis Entropy; Machine Learning; Restricted Boltzmann Machines; Kaniadakis Entropy; Kaniadakis Entropy; Machine Learning; Machine Learning; Restricted Boltzmann Machines; Restricted Boltzmann Machines
Made available in DSpace on 2020-12-12T02:27:01Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-07-01
Restricted Boltzmann Machines achieved notorious popularity in the scientific community in the last decade due to outstanding results in a wide range of applications and also for providing the required mechanisms to build successful deep learning models, i.e., Deep Belief Networks and Deep Boltzmann Machines. However, their main bottleneck is related to the learning step, which is usually time-consuming. In this paper, we introduce a Sigmoid-like family of functions based on the Kaniadakis entropy formulation in the context of the RBM learning procedure. Experiments concerning binary image reconstruction are conducted in four public datasets to evaluate the robustness of the proposed approach. The results suggest that such a family of functions is suitable to increase the convergence rate when compared to standard functions employed by the research community.
Department of Computing UFSCar - Federal University of São Carlos
School of Sciences UNESP - São Paulo State University
School of Sciences UNESP - São Paulo State University
