Ball, John E.
Anderson, Derek T.
Date of Degree
Graduate Thesis - Open Access
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.
Reza, Tasmia, "Object Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems" (2018). Theses and Dissertations MSU. 3341.