A comparative study to deep learning for pattern recognition, by using online and batch learning; taking cybersecurity as a case
Choukri Djellali, Mehdi Adda, Mohamed Tarik Moutacalli · 2019
Many models have been proposed to address deep learning problem. Most deep learning models are influenced by presentation order, complex shapes, architecture configuration and learning instability. This paper provides comparative study to deep learning for pattern recognition. Two types of supervised learning techniques were tested which are used for comparison purpose. They correspond to Batch Gradient Descent and Stochastic Gradient Descent. In order to obtain an accurate results with both methods, we used a re-sampling method based on k-fold cross-validation. Experimental Results show that Stochastic Gradient Descent gives good results in comparison to Batch Gradient Descent. The recognition accuracies are seen to improve significantly when Stochastic Gradient Descent is applied for intrusion detection.