mirror of
https://github.com/MartinThoma/LaTeX-examples.git
synced 2025-04-25 06:18:05 +02:00
Related Work hinzugefügt
This commit is contained in:
Martin Thoma
parent
0bae80d4b5
commit
21e710de6f
5 changed files with 150 additions and 11 deletions
Binary file not shown.
|
|
@ -61,6 +61,9 @@
|
|||
\section{Einleitung}
|
||||
\input{Einleitung}
|
||||
|
||||
\section{Related Work}
|
||||
\input{Related-Work}
|
||||
|
||||
\section{DYCOS}
|
||||
\input{DYCOS-Algorithmus}
|
||||
|
||||
|
|
|
|||
|
|
@ -99,8 +99,7 @@ Graphen.
|
|||
Die Vokabularbestimmung kann zu jedem Zeitpunkt $t$ durchgeführt
|
||||
werden, muss es aber nicht.
|
||||
|
||||
In \cref{alg:DYCOS} wird der DYCOS-Algorithmus als
|
||||
Pseudocode vorgestellt:
|
||||
In \cref{alg:DYCOS} steht der DYCOS-Algorithmus in Form von Pseudocode:
|
||||
In \cref{alg1:l8} wird für jeden unbeschrifteten Knoten
|
||||
durch die folgenden Zeilen eine Beschriftung gewählt.
|
||||
|
||||
|
|
|
|||
24
documents/Proseminar-Netzwerkanalyse/Related-Work.tex
Normal file
24
documents/Proseminar-Netzwerkanalyse/Related-Work.tex
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
%!TEX root = Ausarbeitung-Thoma.tex
|
||||
Sowohl das Problem der Knotenklassifikation, als auch das der Textklassifikation,
|
||||
wurde bereits in verschiedenen Kontexten. Jedoch scheien bisher entweder nur die Struktur des zugrundeliegenden Graphen oder nur Eigenschaften der Texte verwendet worden zu sein.
|
||||
|
||||
So werden in \cite{bhagat,szummer} unter anderem Verfahren zur Knotenklassifikation
|
||||
beschrieben, die wie der in \cite{aggarwal2011} vorgestellte DYCOS-Algorithmus,
|
||||
um den es in dieser Ausarbeitung geht, auch auf Random Walks basieren.
|
||||
|
||||
Obwohl es auch zur Textklassifikation einige Paper gibt \cite{Zhu02learningfrom,Jiang2010302}, geht doch keines davon auf den Spezialfall der Textklassifikation
|
||||
mit einem zugrundeliegenden Graphen ein.
|
||||
|
||||
Die vorgestellten Methoden zur Textklassifikation variieren außerdem sehr stark.
|
||||
Es gibt Verfahren, die auf dem bag-of-words-Modell basieren \cite{Ko:2012:STW:2348283.2348453}
|
||||
wie es auch im DYCOS-Algorithmus verwendet wird. Aber es gibt auch Verfahren,
|
||||
die auf dem Expectation-Maximization-Algorithmus basieren \cite{Nigam99textclassification}
|
||||
oder Support Vector Machines nutzen \cite{Joachims98textcategorization}.
|
||||
|
||||
Es wäre also gut Vorstellbar, die Art und Weise wie die Texte in die Klassifikation
|
||||
des DYCOS-Algorithmus einfließen zu variieren. Allerdings ist dabei darauf hinzuweisen,
|
||||
dass die im Folgeden vorgestellte Verwendung der Texte sowohl einfach zu implementieren
|
||||
ist und nur lineare Vorverarbeitungszeit in Anzahl der Wörter des Textes hat,
|
||||
als auch es erlaubt einzelne
|
||||
Knoten zu klassifizieren, wobei der Graph nur lokal um den zu klassifizerenden
|
||||
Knoten betrachten werden muss.
|
||||
|
|
@ -45,6 +45,17 @@
|
|||
crossref = {DBLP:conf/kdd/2007web},
|
||||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||||
}
|
||||
|
||||
@article{DBLP:journals/corr/abs-1101-3291,
|
||||
author = {Smriti Bhagat AND Graham Cormode AND S. Muthukrishnan},
|
||||
title = {Node Classification in Social Networks},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1101.3291},
|
||||
year = {2011},
|
||||
ee = {http://arxiv.org/abs/1101.3291},
|
||||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||||
}
|
||||
|
||||
@proceedings{DBLP:conf/kdd/2007web,
|
||||
editor = {Haizheng Zhang AND
|
||||
Myra Spiliopoulou AND
|
||||
|
|
@ -109,14 +120,14 @@
|
|||
}
|
||||
|
||||
@MASTERSTHESIS{Lavesson,
|
||||
AUTHOR = {Lavesson, Niklas},
|
||||
TITLE = {Evaluation and analysis of supervised learning algorithms and classifiers},
|
||||
SCHOOL = {Blekinge Institute of Technology},
|
||||
TYPE = {Diploma Thesis},
|
||||
AUTHOR = {Lavesson, Niklas},
|
||||
TITLE = {Evaluation and analysis of supervised learning algorithms and classifiers},
|
||||
SCHOOL = {Blekinge Institute of Technology},
|
||||
TYPE = {Diploma Thesis},
|
||||
ADDRESS = {Sweden},
|
||||
MONTH = DEC,
|
||||
YEAR = 2006,
|
||||
PDF = {http://www.bth.se/fou/Forskinfo.nsf/Sok/c655a0b1f9f88d16c125714c00355e5d/$file/Lavesson_lic.pdf}
|
||||
MONTH = DEC,
|
||||
YEAR = 2006,
|
||||
PDF = {http://www.bth.se/fou/Forskinfo.nsf/Sok/c655a0b1f9f88d16c125714c00355e5d/$file/Lavesson_lic.pdf}
|
||||
}
|
||||
|
||||
@article{Stone1974,
|
||||
|
|
@ -157,8 +168,6 @@ ption. The examples used to illustrate the application are drawn from the proble
|
|||
address = {San Francisco, CA, USA},
|
||||
}
|
||||
|
||||
|
||||
|
||||
@incollection{szummer,
|
||||
title = {Partially labeled classification with Markov random walks},
|
||||
author = {Martin Szummer and Jaakkola, Tommi},
|
||||
|
|
@ -168,3 +177,107 @@ pages = {945--952},
|
|||
year = {2001},
|
||||
url = {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips14/AA36.pdf},
|
||||
}
|
||||
|
||||
@incollection{dynamic,
|
||||
title ={Dynamic Label Propagation in Social Networks},
|
||||
author ={Du, Juan AND Zhu, Feida AND Lim, Ee-Peng},
|
||||
booktitle ={Database Systems for Advanced Applications},
|
||||
editor ={Meng, Weiyi AND Feng, Ling AND Bressan, Stéphane AND Winiwarter, Werner AND Song, Wei},
|
||||
pages ={194-209},
|
||||
year ={2013},
|
||||
isbn ={978-3-642-37449-4},
|
||||
volume ={7826},
|
||||
series ={Lecture Notes in Computer Science},
|
||||
doi ={10.1007/978-3-642-37450-0_14},
|
||||
url ={http://dx.doi.org/10.1007/978-3-642-37450-0_14},
|
||||
publisher ={Springer Berlin Heidelberg},
|
||||
}
|
||||
|
||||
@TECHREPORT{Zhu02learningfrom,
|
||||
author = {Xiaojin Zhu and Zoubin Ghahramani},
|
||||
title = {Learning from Labeled and Unlabeled Data with Label Propagation},
|
||||
institution = {Carnegie Mellon University},
|
||||
year = {2002}
|
||||
}
|
||||
|
||||
@TECHREPORT{Seeger01learningwith,
|
||||
author = {Matthias Seeger},
|
||||
title = {Learning with Labeled and Unlabeled Data},
|
||||
institution = {University of Edinburgh},
|
||||
year = {2001}
|
||||
}
|
||||
|
||||
@article{Kazienko2012199,
|
||||
title = "Label-dependent node classification in the network ",
|
||||
journal = "Neurocomputing ",
|
||||
volume = "75",
|
||||
number = "1",
|
||||
pages = "199 - 209",
|
||||
year = "2012",
|
||||
note = "Brazilian Symposium on Neural Networks (SBRN 2010) International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) ",
|
||||
issn = "0925-2312",
|
||||
doi = "http://dx.doi.org/10.1016/j.neucom.2011.04.047",
|
||||
url = "http://www.sciencedirect.com/science/article/pii/S092523121100508X",
|
||||
author = "Przemyslaw Kazienko and Tomasz Kajdanowicz",
|
||||
keywords = "Classification",
|
||||
keywords = "Node classification",
|
||||
keywords = "Label-dependent classification",
|
||||
keywords = "Label-dependent features",
|
||||
keywords = "Collective classification",
|
||||
keywords = "Classification in networks",
|
||||
keywords = "\{LDBootstrapping\}",
|
||||
keywords = "\{LDGibbs\}",
|
||||
keywords = "Bootstrapping",
|
||||
keywords = "Gibbs sampling "
|
||||
}
|
||||
|
||||
@MISC{Joachims98textcategorization,
|
||||
author = {Thorsten Joachims},
|
||||
title = {Text Categorization with Support Vector Machines: Learning with Many Relevant Features},
|
||||
year = {1998}
|
||||
}
|
||||
|
||||
@INPROCEEDINGS{Nigam99textclassification,
|
||||
author = {Kamal Nigam and Andrew Kachites Mccallum and Sebastian Thrun and Tom Mitchell},
|
||||
title = {Text Classification from Labeled and Unlabeled Documents using EM},
|
||||
booktitle = {Machine Learning},
|
||||
year = {1999},
|
||||
pages = {103--134}
|
||||
}
|
||||
|
||||
@article{Jiang2010302,
|
||||
title = "Text classification using graph mining-based feature extraction ",
|
||||
journal = "Knowledge-Based Systems ",
|
||||
volume = "23",
|
||||
number = "4",
|
||||
pages = "302 - 308",
|
||||
year = "2010",
|
||||
note = "Artificial Intelligence 2009 AI-2009 The 29th \{SGAI\} International Conference on Artificial Intelligence ",
|
||||
issn = "0950-7051",
|
||||
doi = "http://dx.doi.org/10.1016/j.knosys.2009.11.010",
|
||||
url = "http://www.sciencedirect.com/science/article/pii/S095070510900152X",
|
||||
author = "Chuntao Jiang and Frans Coenen and Robert Sanderson and Michele Zito",
|
||||
keywords = "Text classification",
|
||||
keywords = "Graph representation",
|
||||
keywords = "Graph mining",
|
||||
keywords = "Weighted graph mining",
|
||||
keywords = "Feature extraction "
|
||||
}
|
||||
|
||||
@inproceedings{Ko:2012:STW:2348283.2348453,
|
||||
author = {Ko, Youngjoong},
|
||||
title = {A Study of Term Weighting Schemes Using Class Information for Text Classification},
|
||||
booktitle = {Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval},
|
||||
series = {SIGIR '12},
|
||||
year = {2012},
|
||||
isbn = {978-1-4503-1472-5},
|
||||
location = {Portland, Oregon, USA},
|
||||
pages = {1029--1030},
|
||||
numpages = {2},
|
||||
url = {http://doi.acm.org/10.1145/2348283.2348453},
|
||||
doi = {10.1145/2348283.2348453},
|
||||
acmid = {2348453},
|
||||
publisher = {ACM},
|
||||
address = {New York, NY, USA},
|
||||
keywords = {idf, term weighting, text classification},
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue