Denninger, Maximilian (2017) An efficient probabilistic online classification approach for object recognition with random forests. Master's, Technische Universität München.
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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/ | ||||||
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Document Type: | Thesis (Master's) | ||||||
Title: | An efficient probabilistic online classification approach for object recognition with random forests | ||||||
Authors: |
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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 System Technology | ||||||
DLR - Research area: | Raumfahrt | ||||||
DLR - Program: | R SY - Space System Technology | ||||||
DLR - Research theme (Project): | R - Vorhaben Multisensorielle Weltmodellierung (old) | ||||||
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 |
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