Preventing Model Overfitting and Underfitting in Convolutional Neural Networks

2. Background Research

Cognitive computing and cognitive architectures recently emerged as powerful tools to tackle complex large-scale real-life problems in the presence of uncertainty and variable data quality (Tian et al. 2012), (Wang et al., 2013), (Wang et al., 2016). Popular approaches that assist in building cognitive models, which can simulate human thought process, include deep machine learning methods, artificial neural networks (ANN), convolution neural networks (CNN), neuro-linguistic programming (NLP) and sentiment analysis. They have been successfully applied to various intelligent systems in the fields of computer graphics, robotics, knowledge representation, virtual reality, situation awareness, decision-support systems, medicine and many other areas (Wang et.al., 2017), (Gavrilova et al., 2017), (Montero-Obasso et al., 2012). One of the fastest growing domains where notable progress has been made using cognitive, fuzzy and multi-modal architectures is biometric security and image processing (Browne & Ghidary, 2003), (Han & Bhanu, 2006), (Monwar et al., 2011), (Yuan et al., 2008). Over past couple of years, there has been a significant surge in adapting machine-learning methods for image recognition. The introduction of CNN created excitement in image processing research community, with new opportunities to significantly increase image identification rate with a fraction of computational resources, thus making the recognition process more accurate and less resource demanding.