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Journal articles on the topic 'Restricted Boltzmann machines (RBM)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Aoki, Ken-Ichi, and Tamao Kobayashi. "Restricted Boltzmann machines for the long range Ising models." Modern Physics Letters B 30, no. 34 (2016): 1650401. http://dx.doi.org/10.1142/s0217984916504017.

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We set up restricted Boltzmann machines (RBM) to reproduce the long range Ising (LRI) models of the Ohmic type in one dimension. The RBM parameters are tuned by using the standard machine learning procedure with an additional method of configuration with probability (CwP). The quality of resultant RBM is evaluated through the susceptibility with respect to the magnetic external field. We compare the results with those by block decimation renormalization group (BDRG) method, and our RBM clear the test with satisfactory precision.

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Côté, Marc-Alexandre, and Hugo Larochelle. "An Infinite Restricted Boltzmann Machine." Neural Computation 28, no. 7 (2016): 1265–88. http://dx.doi.org/10.1162/neco_a_00848.

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We present a mathematical construction for the restricted Boltzmann machine (RBM) that does not require specifying the number of hidden units. In fact, the hidden layer size is adaptive and can grow during training. This is obtained by first extending the RBM to be sensitive to the ordering of its hidden units. Then, with a carefully chosen definition of the energy function, we show that the limit of infinitely many hidden units is well defined. As with RBM, approximate maximum likelihood training can be performed, resulting in an algorithm that naturally and adaptively adds trained hidden uni

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Li, Yu, Yuan Zhang, and Yue Ji. "Privacy-Preserving Restricted Boltzmann Machine." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/138498.

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With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the a

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Bulso, Nicola, and Yasser Roudi. "Restricted Boltzmann Machines as Models of Interacting Variables." Neural Computation 33, no. 10 (2021): 2646–81. http://dx.doi.org/10.1162/neco_a_01420.

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Abstract We study the type of distributions that restricted Boltzmann machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they impose on observed binary nodes. We report an exact expression for these marginals in the form of a model of interacting binary variables with the explicit form of the interactions depending on the hidden node activation function. We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distribu

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Assis, Carlos A. S., Eduardo J. Machado, Adriano C. M. Pereira, and Eduardo G. Carrano. "Hybrid deep learning approach for financial time series classification." Revista Brasileira de Computação Aplicada 10, no. 2 (2018): 54–63. http://dx.doi.org/10.5335/rbca.v10i2.7904.

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This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the combined RBM + SVM techniques showed better performance when compared to the isolated SVM, which suggests that the proposed approach can be suitable for the considered application.

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Cheng, Song, Jing Chen, and Lei Wang. "Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines." Entropy 20, no. 8 (2018): 583. http://dx.doi.org/10.3390/e20080583.

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We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states, respectively. Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the Restricted Boltzmann Machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches. We also estimate the classical mutual i

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Cho, KyungHyun, Tapani Raiko, and Alexander Ilin. "Enhanced Gradient for Training Restricted Boltzmann Machines." Neural Computation 25, no. 3 (2013): 805–31. http://dx.doi.org/10.1162/neco_a_00397.

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Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representation. An equivalent RBM can be obtained by flipping some bits and changing the weights and biases accordingly, but traditional learning rules are not invariant to such transformations. Without careful tuni

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Zhang, Jingshuai, Yuanxin Ouyang, Weizhu Xie, Wenge Rong, and Zhang Xiong. "Context-aware restricted Boltzmann machine meets collaborative filtering." Online Information Review 44, no. 2 (2018): 455–76. http://dx.doi.org/10.1108/oir-02-2017-0069.

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Purpose The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy. Design/methodology/approach The proposed approach is based on the RBM. The authors employ user occupation information as a context to design

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Wang, Xi-Li, and Fen Chen. "Shape Modeling Based on Convolutional Restricted Boltzmann Machines." MATEC Web of Conferences 173 (2018): 01022. http://dx.doi.org/10.1051/matecconf/201817301022.

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This paper proposes a kind of shape model based on convolutional restricted Boltzmann machines(CRBM), which can be used to assist the task of image target detection and classification. The CRBM is a generative model that can model shapes through the generative capabilities of the model. This paper presents the visual representation, construction process and training method of the model construction. This paper does experiments on the Weizmann Horse dataset. The results show that, compared with RBM, although the training time of this model is slightly longer, the test time of the model is simil

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Wei, Jiangshu, Jiancheng Lv, and Zhang Yi. "A New Sparse Restricted Boltzmann Machine." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (2019): 1951004. http://dx.doi.org/10.1142/s0218001419510042.

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Although existing sparse restricted Boltzmann machine (SRBM) can make some hidden units activated, the major disadvantage is that the sparseness of data distribution is usually overlooked and the reconstruction error becomes very large after the hidden unit variables become sparse. Different from the SRBMs which only incorporate a sparse constraint term in the energy function formula from the original restricted Boltzmann machine (RBM), an energy function constraint SRBM (ESRBM) is proposed in this paper. The proposed ESRBM takes into account the sparseness of the data distribution so that the

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Le Roux, Nicolas, and Yoshua Bengio. "Representational Power of Restricted Boltzmann Machines and Deep Belief Networks." Neural Computation 20, no. 6 (2008): 1631–49. http://dx.doi.org/10.1162/neco.2008.04-07-510.

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Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a restricted Boltzmann machine (RBM), used to represent one layer of the model. Restricted Boltzmann machines are interesting because inference is easy in them and because they have been successfully used as building blocks for training deeper models. We first prove that adding hidden units yields strictly

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Dewi, Christine, Rung-Ching Chen, Hendry, and Hsiu-Te Hung. "Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification." Vietnam Journal of Computer Science 08, no. 03 (2021): 417–32. http://dx.doi.org/10.1142/s2196888821500184.

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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 l

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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations

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Schmah, Tanya, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, and Stephen C. Strother. "Comparing Classification Methods for Longitudinal fMRI Studies." Neural Computation 22, no. 11 (2010): 2729–62. http://dx.doi.org/10.1162/neco_a_00024.

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We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across sub

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Montufar, Guido, and Nihat Ay. "Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines." Neural Computation 23, no. 5 (2011): 1306–19. http://dx.doi.org/10.1162/neco_a_00113.

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We improve recently published results about resources of restricted Boltzmann machines (RBM) and deep belief networks (DBN) required to make them universal approximators. We show that any distribution [Formula: see text] on the set [Formula: see text] of binary vectors of length [Formula: see text] can be arbitrarily well approximated by an RBM with [Formula: see text] hidden units, where [Formula: see text] is the minimal number of pairs of binary vectors differing in only one entry such that their union contains the support set of [Formula: see text]. In important cases this number is half t

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Tubiana, Jérôme, Simona Cocco, and Rémi Monasson. "Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins." Neural Computation 31, no. 8 (2019): 1671–717. http://dx.doi.org/10.1162/neco_a_01210.

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A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizing the patterns of coevolution between amino acids in protein sequences and for designing new sequences. Here, we study how the nature of the features learned by RBM changes with its defining parameters, such as the dimensionality of the representations (size of the hidden layer) and the sparsity of the features. We show that for adequate values of

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Wang, Qianglong, Xiaoguang Gao, Kaifang Wan, Fei Li, and Zijian Hu. "A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy." Mathematical Problems in Engineering 2020 (March 20, 2020): 1–19. http://dx.doi.org/10.1155/2020/4206457.

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The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a f

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Chen, Zhong, Shengwu Xiong, Zhixiang Fang, Ruiling Zhang, Xiangzhen Kong, and Yi Rong. "Topologically Ordered Feature Extraction Based on Sparse Group Restricted Boltzmann Machines." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/267478.

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How to extract topologically ordered features efficiently from high-dimensional data is an important problem of unsupervised feature learning domains for deep learning. To address this problem, we propose a new type of regularization for Restricted Boltzmann Machines (RBMs). Adding two extra terms in the log-likelihood function to penalize the group weights and topologically ordered factors, this type of regularization extracts topologically ordered features based on sparse group Restricted Boltzmann Machines (SGRBMs). Therefore, it encourages an RBM to learn a much smoother probability distri

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Rully Widiastutik, Lukman Zaman P. C. S. W, and Joan Santoso. "Peringkasan Teks Ekstraktif pada Dokumen Tunggal Menggunakan Metode Restricted Boltzmann Machine." Journal of Intelligent System and Computation 1, no. 2 (2019): 58–64. http://dx.doi.org/10.52985/insyst.v1i2.84.

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Penelitian yang dilakukan yaitu menghasilkan peringkasan teks ekstratif secara otomatis yang dapat membantu menghasilkan dokumen yang lebih pendek dari dokumen aslinya dengan cara mengambil kalimat penting dari dokumen sehingga pembaca dapat memahami isi dokumen dengan cepat tanpa membaca secara keseluruhan. Dataset yang digunakan sebanyak 30 dokumen tunggal teks berita berbahasa Indonesia yang diperoleh dari www.kompas.com pada kategori tekno. Dalam penelitian ini, digunakan sepuluh fitur yaitu posisi kalimat, panjang kalimat, data numerik, bobot kalimat, kesamaan antara kalimat dan centroid,

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Bao, Lin, Xiaoyan Sun, Yang Chen, Guangyi Man, and Hui Shao. "Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems." Complexity 2018 (November 1, 2018): 1–13. http://dx.doi.org/10.1155/2018/2609014.

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A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probab

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Song, Haifeng, Guangsheng Chen, and Weiwei Yang. "An Image Classification Algorithm and its Parallel Implementation Based on ANL-RBM." Journal of Information Technology Research 11, no. 3 (2018): 29–46. http://dx.doi.org/10.4018/jitr.2018070103.

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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 f

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Khan, Umair, Pooyan Safari, and Javier Hernando. "Restricted Boltzmann Machine Vectors for Speaker Clustering and Tracking Tasks in TV Broadcast Shows." Applied Sciences 9, no. 13 (2019): 2761. http://dx.doi.org/10.3390/app9132761.

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Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained to transform utterances into a vector based representation. Because of the lack of data for a test speaker, we propose RBM adaptation to a global model. First, the global model—which is referred to as universal RBM—is trained with all the available background data. Then an adapted RBM model is trained with the data of eac

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Liu, Junhui, Yajuan Jia, Yaya Wang, and Petr Dolezel. "Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network." Journal of Advanced Transportation 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8859891.

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Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained

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Koshka, Yaroslav, Dilina Perera, Spencer Hall, and M. A. Novotny. "Determination of the Lowest-Energy States for the Model Distribution of Trained Restricted Boltzmann Machines Using a 1000 Qubit D-Wave 2X Quantum Computer." Neural Computation 29, no. 7 (2017): 1815–37. http://dx.doi.org/10.1162/neco_a_00974.

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The possibility of using a quantum computer D-Wave 2X with more than 1000 qubits to determine the global minimum of the energy landscape of trained restricted Boltzmann machines is investigated. In order to overcome the problem of limited interconnectivity in the D-Wave architecture, the proposed RBM embedding combines multiple qubits to represent a particular RBM unit. The results for the lowest-energy (the ground state) and some of the higher-energy states found by the D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm. In many cases, the D-Wave machine su

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He, Xiao-hui, Dong Wang, Yan-feng Li, and Chun-hua Zhou. "A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2957083.

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To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM). Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form represent

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Larochelle, Hugo, Yoshua Bengio, and Joseph Turian. "Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest." Neural Computation 22, no. 9 (2010): 2285–307. http://dx.doi.org/10.1162/neco_a_00014.

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We investigate the problem of estimating the density function of multivariate binary data. In particular, we focus on models for which computing the estimated probability of any data point is tractable. In such a setting, previous work has mostly concentrated on mixture modeling approaches. We argue that for the problem of tractable density estimation, the restricted Boltzmann machine (RBM) provides a competitive framework for multivariate binary density modeling. With this in mind, we also generalize the RBM framework and present the restricted Boltzmann forest (RBForest), which replaces the

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Jiang, Yun, Junyu Zhuo, Juan Zhang, and Xiao Xiao. "The optimization of parallel convolutional RBM based on Spark." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (2019): 1940011. http://dx.doi.org/10.1142/s0219691319400113.

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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 traditi

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Fischer, Asja, and Christian Igel. "Bounding the Bias of Contrastive Divergence Learning." Neural Computation 23, no. 3 (2011): 664–73. http://dx.doi.org/10.1162/neco_a_00085.

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Optimization based on k-step contrastive divergence (CD) has become a common way to train restricted Boltzmann machines (RBMs). The k-step CD is a biased estimator of the log-likelihood gradient relying on Gibbs sampling. We derive a new upper bound for this bias. Its magnitude depends on k, the number of variables in the RBM, and the maximum change in energy that can be produced by changing a single variable. The last reflects the dependence on the absolute values of the RBM parameters. The magnitude of the bias is also affected by the distance in variation between the modeled distribution an

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Susilawati, Susilawati, and Muhathir Muhathir. "Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM)." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 2, no. 2 (2019): 77. http://dx.doi.org/10.31289/jite.v2i2.2162.

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<p>Restricted boltzmann machines (RBM) merupakan algoritma pembelajaran jaringan syaraf tanpa pengawaas (<em>unsupervised learning</em>) yang hanya terdiri dari dua lapisan yang <em>visible layer</em> dan <em>hidden layer</em>. Kinerja RBM sangat dipengaruhi oleh parameter-parameternya seperti fungsi aktivasi yang digunakan untuk mengaktifkan neuron pada jaringan dan <em>learning rate</em> serta <em>momentum</em> untuk mempercepat proses pembelajaran. Pemilihan fungsi aktivasi yang tepat sangat mempengaruhi kinerja dalam menentu

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Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. "Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine." Entropy 20, no. 11 (2018): 809. http://dx.doi.org/10.3390/e20110809.

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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types

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Savitha, Ramasamy, ArulMurugan Ambikapathi, and Kanagasabai Rajaraman. "Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation." Applied Soft Computing 92 (July 2020): 106278. http://dx.doi.org/10.1016/j.asoc.2020.106278.

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Dai, Xiaoai, Junying Cheng, Yu Gao, et al. "Deep Belief Network for Feature Extraction of Urban Artificial Targets." Mathematical Problems in Engineering 2020 (May 30, 2020): 1–13. http://dx.doi.org/10.1155/2020/2387823.

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Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters

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Li, Ruifan, Fangxiang Feng, Xiaojie Wang, Peng Lu, and Bohan Li. "Obtaining Cross Modal Similarity Metric with Deep Neural Architecture." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/293176.

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Analyzing complex system with multimodal data, such as image and text, has recently received tremendous attention. Modeling the relationship between different modalities is the key to address this problem. Motivated by recent successful applications of deep neural learning in unimodal data, in this paper, we propose a computational deep neural architecture, bimodal deep architecture (BDA) for measuring the similarity between different modalities. Our proposed BDA architecture has three closely related consecutive components. For image and text modalities, the first component can be constructed

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Cui, Zongyong, Zongjie Cao, Jianyu Yang, and Hongliang Ren. "Hierarchical Recognition System for Target Recognition from Sparse Representations." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/527095.

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A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be o

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Aldwairi, Tamer, Dilina Perera, and Mark A. Novotny. "Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms." Electronics 9, no. 7 (2020): 1167. http://dx.doi.org/10.3390/electronics9071167.

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The amassed growth in the size of data, caused by the advancement of technologies and the use of internet of things to collect and transmit data, resulted in the creation of large volumes of data and an increasing variety of data types that need to be processed at very high speeds so that we can extract meaningful information from these massive volumes of unstructured data. The process of mining this data is very challenging since a lot of the data suffers from the problem of high dimensionality. The quandary of high dimensionality represents a great challenge that can be controlled through th

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Mahmoud, Abeer M., and Hanen Karamti. "Classifying a type of brain disorder in children: an effective fMRI based deep attempt." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 260. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp260-269.

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<span>Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 1

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Guo, Xian, Zhang, Li, and Ren. "Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft." Sensors 19, no. 17 (2019): 3682. http://dx.doi.org/10.3390/s19173682.

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To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM meth

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Behera, Dayal Kumar, Madhabananda Das, Subhra Swetanisha, and Prabira Kumar Sethy. "Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 445. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp445-452.

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<span>One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than o

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Liu, Qinghua, Lu Sun, Alain Kornhauser, Jiahui Sun, and Nick Sangwa. "Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm." Sensor Review 39, no. 6 (2019): 733–42. http://dx.doi.org/10.1108/sr-05-2018-0132.

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Purpose To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small. Design/methodology/approach The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power sp

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Huang, Jizhong, and Yepeng Guan. "Dropout Deep Belief Network Based Chinese Ancient Ceramic Non-Destructive Identification." Sensors 21, no. 4 (2021): 1318. http://dx.doi.org/10.3390/s21041318.

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A non-destructive identification method was developed here based on dropout deep belief network in multi-spectral data of ancient ceramic. A fractional differential algorithm was proposed to enhance the spectral details by making use of the difference between the first and second-order differential pre-process spectral data. An unsupervised multi-layer restricted Boltzmann machine (RBM) was employed to extract some high-level features during pre-training. Some weight and bias values trained by RBM were used to initialize a back propagation (BP) neural network. The RBM deep belief network was f

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Wu, Xin-Jie, Ming-Da Xu, Chang-Di Li, Chong Ju, Qian Zhao, and Shi-Xing Liu. "Research on image reconstruction algorithms based on autoencoder neural network of Restricted Boltzmann Machine (RBM)." Flow Measurement and Instrumentation 80 (August 2021): 102009. http://dx.doi.org/10.1016/j.flowmeasinst.2021.102009.

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Yu, He, Zaike Tian, Hongru Li, Baohua Xu, and Guoqing An. "A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps." International Journal of Acoustics and Vibration 25, no. 3 (2020): 373–82. http://dx.doi.org/10.20855/ijav.2020.25.31669.

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Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected gr

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Nguyen, Kuong Trong, Eiji Uchino, and Noriaki Suetake. "Recognition of Coronary Atherosclerotic Plaque Tissue on Intravascular Ultrasound Images by Using Misclassification Sensitive Training of Discriminative Restricted Boltzmann Machine." Journal of Biomimetics, Biomaterials and Biomedical Engineering 37 (June 2018): 85–93. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.37.85.

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Coronary atherosclerotic plaque has been extensively studied in pathological research. Improving the evaluation of vulnerable rupture is important to prevent acute heart failure. Intravascular ultrasound (IVUS) method is one of techniques to acquire information about atherosclerotic plaque, which is backscattered ultrasound signal sensed by IVUS transducer. The vessel structure and tissue components are then characterized in relation to the acquired signals. In this study, eight human coronary vessel sections are involved, and we use discriminative restricted Boltzmann machine (RBM) to classif

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Maldonado-Chan, Mauricio, Andres Mendez-Vazquez, and Ramon Osvaldo Guardado-Medina. "Multimodal Tucker Decomposition for Gated RBM Inference." Applied Sciences 11, no. 16 (2021): 7397. http://dx.doi.org/10.3390/app11167397.

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Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional distribution of one image (the output) given another image (the input). This allows the hidden units of a gated RBM to model the transformations between two successive images. Inference in the model consists in extracting the transformations given a pair of images. However, a fully connected multiplicative network creates cubically many parameters, for

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NIWA, Tadaaki, Keitaro NARUSE, Ryousuke OOE, Masahiro KINOSHITA, Tamotsu MITAMURA, and Takashi KAWAKAMI. "1A1-K02 A Music generation by Associative Memorization Model of The Music Features using Restricted Boltzmann Machine and Conditional RBM(Evolution and Learning for Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _1A1—K02_1—_1A1—K02_3. http://dx.doi.org/10.1299/jsmermd.2014._1a1-k02_1.

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Chen, Shuting, and Dapeng Tan. "A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm." Complexity 2018 (January 4, 2018): 1–21. http://dx.doi.org/10.1155/2018/6264124.

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Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNs-based cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing–artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization

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Le Roux, Nicolas, Nicolas Heess, Jamie Shotton, and John Winn. "Learning a Generative Model of Images by Factoring Appearance and Shape." Neural Computation 23, no. 3 (2011): 593–650. http://dx.doi.org/10.1162/neco_a_00086.

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Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly

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Zhang, Kaiyu, Shanshan Shi, Shu Liu, Junjie Wan, and Lijia Ren. "Research on DBN-based Evaluation of Distribution Network Reliability." E3S Web of Conferences 242 (2021): 03004. http://dx.doi.org/10.1051/e3sconf/202124203004.

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In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN mod

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Joghee Bhojan, Rajkumar, D. Ramyachitra, Subramanian Ganesan, and Ragavi Rajkumar. "A Hybrid Deep Learning Based Visual System for In-Vehicle Safety." European Journal of Engineering Research and Science 4, no. 4 (2019): 43–47. http://dx.doi.org/10.24018/ejers.2019.4.4.1185.

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In the automotive industry, researchers, AI experts, and developers are actively pushing deep learning based approaches for In-vehicle safety. In this research paper, we propose a hybrid deep learning based visual system for providing feedback to the driver in a non-intrusive manner. We describe a hybrid SSD-RBM (Single Shot MultiBox Detector - Restricted Boltzmann Machine) model for face feature identification. In this system, object detection, object tracking, and observations are processed through a full pipeline of image processing and detect the driver's movements and generate a safe and

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Liu, Jianlin, Fenxiong Chen, and Dianhong Wang. "Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks." Sensors 18, no. 12 (2018): 4273. http://dx.doi.org/10.3390/s18124273.

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Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed o

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