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Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

James A. Koziol, Eng M. Tan, Liping Dai, Pengfei Ren, Jian-Ying Zhang
2014 Computational Biology Journal  
We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma.  ...  In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems  ...  We used two methods for the classification problem, logistic regression and restricted Boltzmann machines (RBMs).  ... 
doi:10.1155/2014/418069 fatcat:5e4khu2h6zftlit5e6bsqhxnqi

Minimum Energy Information Fusion in Sensor Networks [article]

George Chapline
1999 arXiv   pre-print
In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of  ...  We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches.  ...  Used as a pattern recognition device the Boltzmann machine has the virtue that high order correlations between different instances of environmental data can be represented and used in the classification  ... 
arXiv:adap-org/9906002v1 fatcat:sncmnwup5rag7jg5p34ge33hdi

Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation

Soojeong Lee, Joon-Hyuk Chang
2018 Applied Sciences  
The deep Boltzmann machine is a state-of-the-art technology in which multiple restricted Boltzmann machines are accumulated.  ...  This study is one of the first to use deep Boltzmann machine-based Dempster–Shafer fusion to classify and estimate blood pressure.  ...  Figure 3 . 3 DBM (deep Boltzmann machine) is built by using RBMs (restricted Boltzmann machine), where W is a weighted parameter [12, 13] .  ... 
doi:10.3390/app9010096 fatcat:3lptuuwmtfhillbjwjmyuyslsi

Multimodal fusion using dynamic hybrid models

Mohamed R. Amer, Behjat Siddiquie, Saad Khan, Ajay Divakaran, Harpreet Sawhney
2014 IEEE Winter Conference on Applications of Computer Vision  
Staged Dynamic Hybrid model -Generative Component: Multimodal Conditional Restricted Boltzmann Machines (MMCRBMs) -Discriminative Component: Conditional Random Field (CRF) • (Hybrid) Allows for  ...  » Audio » Audio Video -Challenges: » Temporal Data from Multiple Heterogeneous Modalities » Multiple Temporal Scales » Missing Data 3 Audio Video © 2013 SRI International Approach We propose a  ... 
doi:10.1109/wacv.2014.6836053 dblp:conf/wacv/AmerSKDS14 fatcat:67qiu7vxcbgqvdc3f7pjg3qguq

Facial Attributes Classification Using Multi-task Representation Learning

Max Ehrlich, Timothy J. Shields, Timur Almaev, Mohamed R. Amer
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
For learning this shared feature representation we use a Restricted Boltzmann Machine (RBM) based model, enhanced with a factored multi-task component to become Multi-Task Restricted Boltzmann Machine  ...  Our approach is not restricted to any type of attributes, however, for this paper we focus only on facial attributes.  ...  We use Restricted Boltzmann Machines (RBMs) [15] as our building block.  ... 
doi:10.1109/cvprw.2016.99 dblp:conf/cvpr/EhrlichSAA16 fatcat:5xdpyy24wffw7c4jakq3gagthi

Spectrum Sensing using Enhanced Restricted Boltzmann Machine for Cognitive Radio Network

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
In this paper, an enhanced restricted Boltzmann machine (ERBM) is presented for spectrum sensing based on RBM.  ...  Researchers have presented machine learning techniques for spectrum sensing, though, challenges exists for the improvement in the throughput, energy efficiency, detection probability and delivery ratio  ...  Section 3 describes the basic of restricted Boltzmann machine algorithm. Section 4 propose enhanced restricted Boltzmann machine algorithm for spectrum sensing.  ... 
doi:10.35940/ijitee.k7822.0991120 fatcat:moux3tii4vfvpoy3yyt4xyvcvm

Recognizing Human Activity in Still Images by Integrating Group-Based Contextual Cues

Zheng Zhou, Kan Li, Xiangjian He
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
A fusion restricted Boltzmann machine, a focal subspace measurement and a cue integration algorithm based on entropy are proposed to enable the GLCIM to integrate most of the relevant local cues and least  ...  We have also proposed a fusion restricted Boltzmann machine and a focal subspace measurement to estimate the interdependency between pairs of persons even if the amount of training data is limited.  ...  Fusion Restricted Boltzmann Machine To fuse the visual features and the layout information, which are quite different in scale, we design a FRBM.  ... 
doi:10.1145/2733373.2806300 dblp:conf/mm/ZhouLH15 fatcat:nyuhdqbe3rbk3mwubw5lw5mk44

Energy disaggregation for real-time building flexibility detection

Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
2016 2016 IEEE Power and Energy Society General Meeting (PESGM)  
Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction.  ...  A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost.  ...  Restricted Boltzmann Machine Restricted Boltzmann Machine is a two-layer generative stochastic neural network which is capable to learn a probability distribution over its set of inputs [22] .  ... 
doi:10.1109/pesgm.2016.7741966 fatcat:kh2odi4etzcpllbucpija5f3v4

Energy Disaggregation for Real-Time Building Flexibility Detection [article]

Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
2016 arXiv   pre-print
Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction.  ...  A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost.  ...  Restricted Boltzmann Machine Restricted Boltzmann Machine is a two-layer generative stochastic neural network which is capable to learn a probability distribution over its set of inputs [22] .  ... 
arXiv:1605.01939v1 fatcat:wkba5nuctvdpxeddwme35ert54

A Survey on Deep Learning for Multimodal Data Fusion

Jing Gao, Peng Li, Zhikui Chen, Jianing Zhang
2020 Neural Computation  
and to motivate new multimodal data fusion techniques of deep learning.  ...  Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.  ...  machine; DMDBN: diagnosis multimodal deep Boltzmann machine; HPMDBN: human pose deep Boltzmann machine; HMDBN: hybrid multimodal deep Boltzmann machine; FMDBN: face multimodal deep Boltzmann machine;  ... 
doi:10.1162/neco_a_01273 pmid:32186998 fatcat:ls27tbkldrbx7n4h7nlc73qyte

Big IoT data mining for real-time energy disaggregation in buildings

Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
2016 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  
Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs) and Disjunctive FFW-CRBM.  ...  We propose a hybrid approach, which combines sparse smart meters with machine learning methods.  ...  Four-Way Conditional Restricted Boltzmann Machines (DFFW-CRBM) [28] .  ... 
doi:10.1109/smc.2016.7844820 dblp:conf/smc/MocanuMNGL16 fatcat:3mlfsi6mhfa7diln7hp5xpfsxu

Capturing Global and Local Dynamics for Human Action Recognition

Siqi Nie, Qiang Ji
2014 2014 22nd International Conference on Pattern Recognition  
In contrast, we propose a novel human action recognition algorithm that is able to capture both global and local dynamics of joint trajectories by combining a Gaussian-Binary restricted Boltzmann machine  ...  We present a method to use RBM as a generative model for multi-class classification.  ...  Restricted Boltzmann machine and its variants are generally used as a tool for feature learning or data pre-processing yet could also be used for modeling the motion data. For example, Wang et al.  ... 
doi:10.1109/icpr.2014.340 dblp:conf/icpr/NieJ14a fatcat:i4dqjzkapzc6valrogvnmtlgyi

Deep Learning Technique for Brain Tumor Detection using Medical Image Fusion

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Brain Tumor detection using Medical image fusion plays an important role in medical field .Using Fusion technique, The medical image can be enhanced to detect the tumor.  ...  The applications used here to detect Brain Tumor are DBN and CNN techniques.  ...  Deep Belief Networks (DBN) is a multi-layer network, In this network each layer is RBM (Restricted Boltzmann Machine). To construct DBN each one is stacked to each other.  ... 
doi:10.35940/ijitee.i1179.0789s219 fatcat:75rqr6cmgbefvisvsu3ztooeli

Protein Function Prediction Using Deep Restricted Boltzmann Machines

Xianchun Zou, Guijun Wang, Guoxian Yu
2017 BioMed Research International  
Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially  ...  Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques.  ...  Methods In this section, we will describe the deep restricted Boltzmann machines to predict missing GO annotations of proteins. Restricted Boltzmann Machine.  ... 
doi:10.1155/2017/1729301 pmid:28744460 pmcid:PMC5506480 fatcat:b4q5pxnlgbdijjqhgiqk6etmci

Adaptive sensor modelling and classification using a continuous restricted Boltzmann machine (CRBM)

Tong Boon Tang, Alan F. Murray
2007 Neurocomputing  
We prove that a Continuous Restricted Boltzmann Machine can model complex data distributions and can autocalibrate against real sensor drift.  ...  This paper presents a neural approach to sensor modelling and classification as the basis of local data fusion in a wireless sensor network. Data distributions are non-Gaussian.  ...  In this paper, we focus on local data fusion using of a generative model, the "Continuous Restricted Boltzmann Machine" (CRBM) and a Single Layer Perceptron (SLP).  ... 
doi:10.1016/j.neucom.2006.11.014 fatcat:dxxtvugw5zfl7ojbmpufwewqf4
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