Results 131 to 140 of about 22,598 (163)

Discriminative Deep Belief Networks for image classification

2010 IEEE International Conference on Image Processing, 2010
This paper presents a novel semi-supervised learning algorithm called Discriminative Deep Belief Networks (DDBN), to address the image classification problem with limited labeled data. We first construct a new deep architecture for classification using a set of Restricted Boltzmann Machines (RBM).
Shusen Zhou, Qingcai Chen, Xiaolong Wang 0001   +2 more
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Complex-Valued Deep Belief Networks

2018
Deep belief networks were among the first models in the deep learning paradigm. Their use for unsupervised pretraining allowed deep neural network architectures to perform better than shallow ones. This paper introduces complex-valued deep belief networks, which can be used for unsupervised pretraining of complex-valued deep neural networks ...
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Forecasting exchange rate with deep belief networks

The 2011 International Joint Conference on Neural Networks, 2011
Forecasting exchange rates is an important financial problem which has received much attention. Nowadays, neural network has become one of the effective tools in this research field. In this paper, we propose the use of a deep belief network (DBN) to tackle the exchange rate forecasting problem.
Jing Chao 0001, Furao Shen, Jinxi Zhao
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Web Classification Using Deep Belief Networks

2014 IEEE 17th International Conference on Computational Science and Engineering, 2014
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing.
Shu Sun, Fang Liu 0026, Jun Liu 0014, Yinan Dou, Hua Yu   +4 more
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Representational Transfer in Deep Belief Networks

2015
A Deep Belief Network is a machine learning approach which can learn hierarchical levels of representations. However, a Deep Belief Network requires large amounts of training examples to learn good representations. Transfer learning is able to improve the performance of learning, especially when the number of training examples is small.
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Dynamic sparsity control in Deep Belief Networks

Intelligent Data Analysis, 2017
A Deep Belief Network (DBN) is a generative probabilistic graphical model that contains many layers of hidden variables and has excelled among deep learning approaches. DBN can extract suitable features, but improving these networks for obtaining features with more discrimination ability is an important issue.
Mohammad Ali Keyvanrad, Mohammad Mehdi Homayounpour   +1 more
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Towards Compact and Explainable Deep Belief Networks

2024 International Joint Conference on Neural Networks (IJCNN)
Vast resources available in the data domain and hardware provided impact impressive achievements in generative AI. A growing interest in IoT and mobile/edge computing is, however, pushing research towards heavy architecture optimization and easy interpretability.
Jan Bronec, Iveta Mrázová
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Auditory Scene Classification with Deep Belief Network

2015
Effective modeling and analyzing of an auditory scene is crucial to many context-aware and content-based multimedia applications. In this paper, we explore the effectiveness of the multiple-layer generative deep neural network model in discovering the underlying higher level and highly non-linear probabilistic representations from acoustic data of the ...
Like Xue, Feng Su
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Deep belief network based intrusion detection techniques: A survey

Expert Systems With Applications, 2021
Insoo Sohn
exaly  

Deep Belief Networks for Spam Filtering

19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), 2007
Grigorios Tzortzis, Aristidis Likas
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