Klasifikacija z utežem agnostičnimi nevronskimi mrežami : magistrsko delo

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Title:	Klasifikacija z utežem agnostičnimi nevronskimi mrežami : magistrsko delo
Authors:	ID Mlakar, Marko (Author) ID Karakatič, Sašo (Mentor) More about this mentor...
Files:	MAG_Mlakar_Marko_2020.pdf (3,89 MB) MD5: 034D29583016FD8F4080A5A7DC88109B PID: 20.500.12556/dkum/25bfbf22-0743-422a-9440-fb7f8031157e
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V magistrskem delu je predstavljena metoda iskanja utežem agnostičnih nevronskih mrež, ki temelji na genetskem algoritmu, imenovanem NeuroEvolution of Augmenting Topologies (NEAT). Evalviranje genomov z vzorčenjem uteži iz fiksne uniformne množice naključnih vrednosti minimizira pomembnost uteži, s čimer je poudarek le na optimizaciji topologije. To omogoča utežem agnostičnim nevronskim mrežam opravljanje različnih nalog brez predhodnega učenja utežnih vrednosti. Naša implementacija je bila prilagojena za povezovanje z odprtokodno knjižnico Scikit-learn, ki smo jo javno objavili v obliki PyPi paketa. V eksperimentalnem delu smo se osredotočili na primerjavo evolucijskih in utežem agnostičnih nevronskih mrež na primeru reševanja klasifikacijskih problemov. Rezultate smo evalvirali z uporabo statističnih metod, ki so pokazale, da utežem agnostične nevronske mreže proizvedejo več skritih nevronov kot evolucijske, vendar uspejo doseči primerljivo točnost zgolj s pravilno topologijo, brez optimizacije uteži.
Keywords:	utežem agnostične nevronske mreže, klasifikacija, nevroevolucija, NEAT
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[M. Mlakar]
Year of publishing:	2020
Number of pages:	XI, 73 f.
PID:	20.500.12556/DKUM-78125
UDC:	004.8(043.2)
COBISS.SI-ID:	45029891
NUK URN:	URN:SI:UM:DK:90QBCR3J
Publication date in DKUM:	01.12.2020
Views:	1094
Downloads:	126
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-ND 4.0, Creative Commons Attribution-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nd/4.0/
Description:	Under the NoDerivatives Creative Commons license one can take a work released under this license and re-distribute it, but it cannot be shared with others in adapted form, and credit must be provided to the author.
Licensing start date:	29.10.2020

Secondary language

Language:	English
Title:	Classification with weight agnostic neural networks
Abstract:	In our master's thesis, we reviewed a search method for weight agnostic neural networks that are based on a genetic algorithm called NeuroEvolution of Augmenting Topologies (NEAT). Evaluating genomes by sampling weights from a fixed uniform random distribution ensures the importance of weights is minimized and the main focus is on optimizing the topology. This gives weight agnostic neural networks an ability to solve different tasks without explicit weight training. Our implementation was made to be compatible with an open-source library called Scikit-learn, and we published it as a public PyPi package. In our experiments, we focused on comparing evolutionary neural networks with weight agnostic neural networks by solving different classification tasks. We evaluated the results with the use of statistical methods which showed that while weight agnostic neural networks created more hidden nodes, their topologies were able to achieve comparable accuracy without optimizing the weights.
Keywords:	weight agnostic neural networks, classification, neuroevolution, NEAT

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