A metaheuristic-driven approach to fine-tune Deep Boltzmann Machines
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Elsevier B.V.
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Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memoryand evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results. (C) 2019 Elsevier B.V. All rights reserved.
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Deep Boltzmann Machine, Meta-heuristic optimization, Machine learning
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Inglês
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Applied Soft Computing. Amsterdam: Elsevier, v. 97, 12 p., 2020.
