Deep Belief Networks and deep learning
| Autor: | Yuming Hua, Hua Zhao, Junhai Guo |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Computer Science::Machine Learning
Restricted Boltzmann machine Artificial neural network Computer science business.industry Deep learning Feature extraction Boltzmann machine Overfitting Machine learning computer.software_genre Statistical classification Deep belief network ComputingMethodologies_PATTERNRECOGNITION Softmax function Unsupervised learning Artificial intelligence business computer Feature learning |
| Zdroj: | Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. |
| Popis: | Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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