Comparative Analysis of Restricted Boltzmann Machine Models for Image Classification

2020 ◽  
pp. 285-296 ◽  
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Hendry ◽  
Hsiu-Te Hung
Keyword(s):  
Comparative Analysis ◽  
Image Classification ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine

The optimization of parallel convolutional RBM based on Spark

2019 ◽  
Vol 17 (02) ◽  
pp. 1940011 ◽  
Author(s):  
Yun Jiang ◽  
Junyu Zhuo ◽  
Juan Zhang ◽  
Xiao Xiao
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Image Classification ◽  
Image Recognition ◽  
Restricted Boltzmann Machine ◽  
Sequential Algorithm ◽  
Boltzmann Machine ◽  
X Ray ◽  
Model Based ◽  
Traditional Algorithm

With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.

scholarly journals Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification

2021 ◽  
pp. 1-16
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Hendry ◽  
Hsiu-Te Hung
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Generative Models ◽  
Restricted Boltzmann Machine ◽  
Model Complexity ◽  
Boltzmann Machine ◽  
Learning Techniques ◽  
Recent Developments ◽  
Hidden Layer

Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs 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 (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.

An Image Classification Algorithm and its Parallel Implementation Based on ANL-RBM

2018 ◽  
Vol 11 (3) ◽  
pp. 29-46
Author(s):  
Haifeng Song ◽  
Guangsheng Chen ◽  
Weiwei Yang
Keyword(s):  
Image Classification ◽  
Parallel Implementation ◽  
Image Data ◽  
Classification Algorithm ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Node Number ◽  
Layer Number ◽  
Hidden Layer ◽  
Entire Network

This article describes how when using Restricted Boltzmann Machine (RBM) algorithm to design the image classification network. The node number in each hidden layer, and the layer number of the entire network are designed by experiments, it increases the complexity for the RBM design. In order to solve the problem, this article proposes an image classification algorithm based on ANL-RBM (Adaptive Nodes and Layers Restricted Boltzmann Machine). The algorithm can automatically calculate the node number in each hidden layer and the layer number of the entire network. It can reduce the complexity for the RBM design. In the meantime, this article has designed the parallel model of the algorithm in the Hadoop platform. The experimental results showed that the image classification algorithm based on an ANL-RBM has a higher execution efficiency, better speedup, better scalability and it is suitable for massive amounts of image data processing.

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