elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

An efficient probabilistic online classification approach for object recognition with random forests

Denninger, Maximilian (2017) An efficient probabilistic online classification approach for object recognition with random forests. Master's, Technische Universität München.

[img] PDF (Masterarbeit)
1MB

Abstract

Online learning on big data sets is still an open problem in the classification of images. Many problems in the real world don't have all data available in the beginning of the training. Therefore it is necessary that the approach is able to integrate new incoming datapoints. Random Forest have been proven to be good in online learning. However the existing approaches do only generate very few trees, which only have a height of five. To overcome this shortcoming this thesis presents several methods to improve the generation of Decision trees, which leads to an algorithm, which can train thousands of tree with a sufficient height. Furthermore the Random Forest were then used in combination with an online sparse Gaussian Process to classify the outliners. These falsely classified points weren't classified correctly by the Random Forest in the first place. This whole approach was then optimized and tested on different datasets. The far most important result was that the presented online approach always yields better results than the offline approach, which is a remarkable result for an online learning approach. Furthermore we outperformed the result from Saffari et al. on the USPS dataset.

Item URL in elib:https://elib.dlr.de/114369/
Document Type:Thesis (Master's)
Title:An efficient probabilistic online classification approach for object recognition with random forests
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Denninger, Maximilianmaximilian.denninger (at) dlr.dehttps://orcid.org/0000-0002-1557-2234
Date:2017
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:93
Status:Published
Keywords:Learning, Online, Random Forest, Classification
Institution:Technische Universität München
Department:Fakultät für Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Denninger, Maximilian
Deposited On:07 Dec 2017 16:25
Last Modified:31 Jul 2019 20:11

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.