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Title:Preprečevanje prekomernega prileganja pri učenju večplastnih nevronskih mrež
Authors:Henčič, Jan (Author)
Strnad, Damjan (Mentor) More about this mentor... New window
Files:.pdf UN_Hencic_Jan_2017.pdf (2,01 MB)
MD5: 387BC0D510BF2B3F4AEDCB2EBCBDBCD3
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo obravnava tematiko strojnega učenja s pomočjo uporabe umetnih nevron- skih mrež. Te so po svojih sposobnostih in načinu delovanja zelo podobne delovanju človeških možganov. Imajo sposobnost akumuliranja znanja s tako imenovanim postop- kom ”učenja”, hkrati pa so sposobne to znanje tudi shranjevati. Pravilnost delovanja mrež se s postopkom učenja, ki se ponavlja iterativno, povečuje. Ena izmed glavnih težav pri učenju nevronskih mrež je pojav prekomernega prileganja, ki se kaže v tem, da mreža ne posplošuje dobro iz učne na testno množico vzorcev. Za preprečevanje tega pojava je bilo razvitih več tehnik, katerih uporaba, učinkovitost in primerjava je predmet pričujočega diplomskega dela.
Keywords:Umetna nevronska mreža, Vzvratno razširjanje, Prekomerno prileganje, Regularizacija
Year of publishing:2017
Publisher:J. Henčič
Source:[Maribor
UDC:004.85.032.26(043.2)
COBISS_ID:20869142 New window
NUK URN:URN:SI:UM:DK:BLNIGGWX
Views:893
Downloads:108
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:17.08.2017

Secondary language

Language:English
Title:Overfitting prevention in training of multilayer neural networks
Abstract:This thesis deals with the subject of machine learning by using artificial neural networks. They are very similar to the human brain in their abilities and way of functioning. They have the capacity to accumulate knowledge through the so-called “learning” process, but they are also able to store this knowledge. The accuracy of artificial neural networks is increased in the process of learning, which is repeated iteratively. One of the main problems in this process is the emergence of overfitting. This is because the network does not generalize well from the learning to the test set. To prevent this phenomenon several different techniques have been developed, the application and effectiveness of which have been analyzed and compared in the present thesis.
Keywords:Artificial neural network, Backpropagation, Overfitting, Regularization


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