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Title:
FILTRIRANJE ELEKTRONSKE POŠTE Z BAYESOVIM KLASIFIKATORJEM
Authors:
ID
Čuček, Ivan
(
Author
)
ID
Strnad, Damjan
(
Mentor
)
More about this mentor...
Files:
VS_Cucek_Ivan_2010.pdf
(34,71 MB)
MD5: B33849049046B81AF610B63959E569A2
PID:
20.500.12556/dkum/abe3ef91-116a-4014-a0fe-3784b56d00ec
Language:
Slovenian
Work type:
Undergraduate thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu smo predstavili implementacijo naivnega Bayesovega klasifikatorja za samodejno razvrščanje elektronske pošte v uporabniško določene kategorije. Klasifikator je izveden v obliki vtičnika za poštni odjemalec Microsoft Outlook, ki ga razvrščanja naučimo na množici učnih primerov ter kasneje med samim delovanjem s popravljanjem napačnih klasifikacij. V uvodu predstavimo verjetnostno sklepanje in klasifikacijo kot algoritem strojnega učenja. Sledi opis uporabljene tehnologije VSTO. V zadnjem delu diplomskega dela je predstavljena struktura implementiranega klasifikatorja in rezultati testiranja učinkovitosti pri različnih velikostih učnih množic.
Keywords:
kategorija
,
verjetnostno sklepanje
,
naivni Bayesov klasifikator
,
VSTO
,
klasifikacija
Place of publishing:
Maribor
Publisher:
[I. Čuček]
Year of publishing:
2010
PID:
20.500.12556/DKUM-17039
UDC:
004.9:519.22(043.2)
COBISS.SI-ID:
14980118
NUK URN:
URN:SI:UM:DK:5SNII0NW
Publication date in DKUM:
12.01.2011
Views:
2073
Downloads:
351
Metadata:
Categories:
KTFMB - FERI
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Secondary language
Language:
English
Title:
ELECTRONIC MAIL FILTERING USING THE BAYES CLASSIFIER
Abstract:
In this diploma work we present the implementation of naive Bayes classifier for automatic classification of electronic mail into a user-defined categories. The classifier is implemented as a plug-in for Microsoft Outlook mail client that is trained with a set of training examples and later during the operation by correcting wrong classifications. In the introduction we present probabilistic reasoning and classification as a machine learning algorithm, followed by the description of the used technology named VSTO. In the last part of diploma work we present the structure of the implemented classifier and the test results of its effectiveness with various sizes of learning sets.
Keywords:
category
,
probabilistic reasoning
,
naive Bayes classifier
,
VSTO
,
classification
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