Multilayer Perceptron Kernel Research Articles

Simulated annealing for SVM parameters optimization in student’s performance prediction

High education is an important and critical part of education all over the world. In last year, the world has been turned increasingly to online education due to the outbreak of the Covid-19 pandemic; therefore, improving this education system became an urgent matter. Online learning systems are a primal environment for acquiring educational data which can be from different sources, especially academic institutions. These data can be mainly used to analyze and extract utilizable information to help in understanding university students’ performance and identifying factors that affect it. To extract some meaningful information from these large volumes of data, academic organizations must mine the data with high accuracy. In this work, three different real datasets were selected, pre-processed, cleaned, and filtered for applying support vector machine (SVM) with multilayer perceptron kernel (MLP kernel) and optimize its parameters using simulated annealing (SA) algorithm to improve the objective function value. While examining the search space, SA has the advantage of escaping from local minima since it offers the chance for accepting the worse neighbor as a solution in a controlled manner. The results show that the designed system can determine the best SVM parameters using SA and therefore presents better model evaluation.

Emotion Analysis on Text Using Multiple Kernel Gaussian...

The ability to discern human emotions is critical for making chatbox behave like humans. Gaussian Process (GP) is a non-parametric Bayesian modeling and can be used to predict the presence of either a single emotion (single-task GP) or multiple emotions (multi-task GP) in natural language text. Employing multiple kernels in GP can enhance the performance of the emotion analysis tasks. The particular choice of kernel functions determines the properties such as smoothness, length scales, sharpness, and amplitude, drawn from the GP prior. Using a specific kernel may be a source of bias and can be avoided by using different kernels together. The default kernel used with GP is a Radial Basis Function (RBF). It is infinitely differentiable; GP with this function has mean square derivatives of all orders and is thus very smooth. The sharpness which occurs in the midst of the smoothness can be detected using the exponential kernel. The multi-layer perceptron kernel has greater generalization for each training example and is good for extrapolation. Our experiments show that, for learning the presence of a single emotion in a natural language sentence (single-task), multiple kernel GP with the sum of RBF and multi-layer perceptron kernels performs better than single kernel GP. Likewise, for learning the presence of several different emotions in a sentence (multi-task), multiple kernel GP with the sum of RBF, exponential and multi-layer perceptron kernels performs better than single kernel GP. Multiple Kernel Gaussian Process also outperforms Convolutional Neural Network (CNN).

Performance of different kernel functions for LS‐SVM‐GWO to estimate flashover voltage of polluted insulators

This work attempts to clarify the potentials of hybrid model based on least-squares support vector machine (LS-SVM) and a novel meta-heuristic algorithm called Grey Wolf optimiser (GWO) in high-voltage applications, considering several kernel functions. The selection of the suitable kernel function and its parameters play an important role in the performance of LS-SVM. For this purpose, GWO is proposed in this study as an efficient optimisation approach to adjust the parameters of various kernel functions such as linear kernel (Lin), radial basis function kernel, polynomial kernel (poly) and multi-layer perceptron kernel. Afterwards, the LS-SVM with the most appropriate kernel function is designed to model flashover voltage of polluted high-voltage insulators. The performance of the developed model is compared with the previous works. The results confirm high capabilities of the proposed hybrid model for the prediction of the flashover voltage of polluted insulators.