Filter Size Optimization on a Convolutional Neural Network Using FGSA
| Autor: | Claudia I. Gonzalez, Gabriela E. Martinez, Yutzil Poma, Patricia Melin |
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| Rok vydání: | 2020 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Contextual image classification Computer science business.industry Computer Science::Neural and Evolutionary Computation Gravitational search algorithm ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Filter (signal processing) Fuzzy logic Convolutional neural network ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) Artificial intelligence business |
| Zdroj: | Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications ISBN: 9783030354442 Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms |
| DOI: | 10.1007/978-3-030-35445-9_29 |
| Popis: | This paper presents an approach to optimize the filter size of a convolutional neural network using the fuzzy gravitational search algorithm (FGSA). The FGSA method has been applied in others works to optimize traditional neural networks achieving good results; for this reason, is used in this paper to optimize the parameters of a convolutional neural network. The optimization of the convolutional neural network is used for the recognition and classification of human faces images. The presented model can be used in any image classification, and in this paper the optimization of convolutional neural network is applied in the CROPPED YALE database. |
| Databáze: | OpenAIRE |
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