A Nonlinear Support Vector Machine Analysis Using Kernel Functions for Nature and Medicine

¹ TIMS Laboratory, FS Tétouan, Abdelmalek Essaadi University, Morocco.
² TIMS Laboratory, ENSA Tétouan, Abdelmalek Essaadi University, Morocco.
³ TIMS Laboratory. FP Larache, Abdelmalek Essaadi University, Morocco.

^* Corresponding author: yassin.benajiba@etu.uae.ac.ma

Abstract

After the emergence of Artificial Intelligence (AI), great developments have taken place in the fields of science, economics, medicine and all other fields that use computer science. Along with the resulting developments in these fields, artificial intelligence has also solved many intractable problems, such as predicting specific serious diseases, determining future product sales, as well as analyzing and studying big data in the shortest possible time … SVM is one of the most important technologies in this field of artificial intelligence that goes into supervised methods, and which every machine learning expert should have in his/her arena. For this reason, in this article, we studied this technique and determined its advantages and disadvantages as well as its fields of application. Next, we applied this technique to three different databases, using four basis change functions, and we compared the results obtained to determine the best way to use the basis change functions.