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.
|Advisor:||Ball, John E.|
|Commitee:||Anderson, Derek T., Tang, Bo|
|School:||Mississippi State University|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Mississippi|
|Source:||MAI 58/01M(E), Masters Abstracts International|
|Subjects:||Computer Engineering, Electrical engineering, Artificial intelligence|
|Keywords:||Advanced driver assistance systems, Convolutional neural networks, LiDAR, Support vector machine|
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