Support vector machines for pattern recognition
Title: Support vector machines for pattern recognition
SNIC Project: SNIC 2019/7-76
Project Type: SNIC Small Compute
Principal Investigator: Mohammad Aslani <>
Affiliation: Högskolan i Gävle
Duration: 2019-10-07 – 2020-11-01
Classification: 10201


Support vector machine is one the best classifiers in the world of machine learning. However, its time complexity O(n^3) hinders obtaining the results when facing with huge datasets. One of the solutions is to select the instances (instance selection) that play a significant role in classification and remove the rest. In this project, we develop a novel and fast instance selection method. [Abstract has been edited after approval (reference number 199743). Contact if you have questions.]