The Self-Organizing Restricted Boltzmann Machine for Deep Representation with the Application on Classification Problems
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Summary
This paper introduces an approach to determine the number of hidden layers and neurons of the deep network automatically during the learning process and acts as a regularization method since the neurons whose weights are lower than the threshold are removed and thus, RBM learns to copy input merely approximate.
- Type
- article
- Published
- 2020-07-01
- Cited by
- 26
- References
- 32
- OpenAlex
- https://openalex.org/W3005559199
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:213388654
Keywords
Deep belief network, Boltzmann machine, Restricted Boltzmann machine, Computer science, Deep learning
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- A Novel Restricted Boltzmann Machine Training Algorithm With Dynamic Tempering Chains
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- A self-organizing deep neuro-fuzzy system approach for classification of kidney cancer subtypes using miRNA genomics data
- Forecasting energy generation in large photovoltaic plants using radial belief neural network
- Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately
- The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities
- An Efficient Internet Traffic Classification System Using Deep Learning for IoT
- The Method of Communication System Fault Diagnosis Based on Deep Belief Net
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- A Deep Learning Based Method for Network Application Classification in Software-Defined IoT
- Genetic Algorithm-Based Feature Selection and Self-Organizing Auto-Encoder (SOAE) For SNP Genomics Data Classification
- Dynamic gaussian deep belief network design and stock market application
- The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students
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