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On the Use of Deep Boltzmann Machines for Road Signs Classification

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Title: On the Use of Deep Boltzmann Machines for Road Signs Classification
Author(s): D'Eramo, Carlo
Advisor(s): Ziebart, Brian
Contributor(s): Berger-Wolf, Tanya; Zanero, Stefano
Department / Program: Computer Science
Graduate Major: Computer Science
Degree Granting Institution: University of Illinois at Chicago
Degree: MS, Master of Science
Genre: Masters
Subject(s): machine learning generative models deep boltzmann machine complex objects recognition
Abstract: The Deep Boltzmann Machine (DBM) has been proved to be one of the most effective machine learning generative models in discriminative tasks. They've been able to overcome other generative, and even discriminative models, on relatively simple tasks, such as digits recognition, and also on more complex tasks such as simple objects recognition. However, there're only a few published results of DBM performances on other complex datasets. In this work we decided to test the efficiency of DBM, and its variant Multi-Prediction Deep Boltzmann Machine (MP-DBM), in classifying a complex dataset composed of road signs and we'll show how we've been able to train both models to reach what, at the best of our knowledge, are the best discriminative results of generative models on the road signs dataset.
Issue Date: 2015-10-21
Genre: thesis
URI: http://hdl.handle.net/10027/19711
Rights Information: Copyright 2015 Carlo D'Eramo
Date Available in INDIGO: 2015-10-21
Date Deposited: 2015-08
 

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