Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming

These demands increase the cost of training and deploying these architectures, and constrain the spectrum of devices that they can be used on. Although deep learning has been very promising in many areas in terms of technology in recent years, it has some problems that need improvement. Even though deep learning can distinguish changes and subtle differences in data with interconnected neural networks, it makes it very difficult to define hyperparameters and determine their values before training the data. For this reason, different pruning methods have been proposed in the literature to reduce the parameters of convolutional networks Han et al. (2016); Hanson & Pratt (1988); LeCun & Cortes (2010); Ström (1997). The common problem of deep learning and pruning algorithms that have been studied in recent years is the decision of the pruning percentage with the heuristic approach at the pruning stage, making the success percentage of the deep learning algorithm dependent on the pruning parameter. On the other hand, the optimization models proposed with zero-norm penalty to ensure sparsity ignore the diversity of the layers as they only take into account the percentage of success. The combination of the layers that are close to each other does not increase the percentage of accuracy. As the primal example of DNNs, Convolutional Neural Networks (CNNs) are feed-forward architectures originally proposed to perform image processing tasks, Li et al. (2021) but they offered such high versatility and capacity that allowed them to be used in many other tasks including time series prediction, signal identification and natural language processing. Inspired by a biological visual perceptron that displays local receptive fields Goodfellow et al. (2016), a CNN uses learnable kernels to extract the relevant features at each processing