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Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition
2020
Applied Computational Intelligence and Soft Computing
To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning
doi:10.1155/2020/8866259
fatcat:gy5d7nnx5vb33inyjopjtofmsa